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GIS-BASED SEARCH THEORY APPLICATION FOR
SEARCH AND RESCUE PLANNING
A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
OF MIDDLE EAST TECHNICAL UNIVERSITY
BY
EMRAH SÖYLEMEZ
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR
THE DEGREE OF MASTER OF SCIENCE IN
GEODETIC AND GEOGRAPHIC INFORMATION TECHNOLOGIES
APRIL 2007
Approval of the Graduate School of Natural and Applied Sciences
Prof. Dr. Canan Özgen
Director
I certify that this thesis satisfies all the requirements as a thesis for the degree of Master of Science.
Assoc. Prof. Dr. H. Şebnem Düzgün
Head of Department
This is to certify that we have read this thesis and that in our opinion it is fully adequate, in scope and quality, as a thesis for the degree of Master of Science.
Assoc. Prof. Dr. H. Şebnem Düzgün
Supervisor
Examining Committee Members
Prof. Dr.Vedat Toprak (METU, GEOG)
Assoc. Prof. H. Şebnem Düzgün (METU, MINE)
Assoc. Prof. Nurünnisa Usul (METU, CE)
Assist. Prof. Zuhal Akyürek (METU, CE)
Major Ali Gerede (TAF)
iii
I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work.
Name, Last name: Emrah, Söylemez Signature:
iv
ABSTRACT
GIS-BASED SEARCH THEORY APPLICATION FOR SEARCH AND
RESCUE PLANNING
SÖYLEMEZ, Emrah
M.Sc., Department of Geodetic and Geographic Information Technologies
Supervisor: Assoc. Prof. Dr. H. Şebnem DÜZGÜN
April 2007, 113 pages
Search and Rescue (SAR) operations aim at finding missing objects with minimum
time in a determined area. There are fundamentally two problems in these
operations. The first problem is assessing highly reliable probability distribution
maps, and the second is determining the search pattern that sweeps the area from
the air as fast as possible.
In this study, geographic information systems (GIS) and multi criteria decision
analysis (MCDA) are integrated and a new model is developed based upon Search
Theory in order to find the position of the missing object as quickly as possible
with optimum resource allocation. Developed model is coded as a search planning
tool for the use of search and rescue planners. Inputs of the model are last known
position of the missing object and related clues about its probable position.
In the developed model, firstly related layers are arranged according to their
priorities based on subjective expert opinion. Then a multi criteria decision method
is selected and each data layer is multiplied by a weight corresponding to search
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expert’s rank. Then a probability map is established according to the result of
MCDA methods. In the second phase, the most suitable search patterns used in
literature are applied based on established probability map. The developed model
is a new approach to shortening the time in SAR operations and finding the
suitable search pattern for the data of different crashes.
Keywords: Search and Rescue, Multi-Criteria Decision Analysis, GIS, Search
Theory, Kütahya
vi
ÖZ
CBS - DESTEKLİ ARAMA TEORİSİ UYGULAMSI VE ARAMA
KURTARMA PLANLAMASI
SÖYLEMEZ, Emrah
Yüksek Lisans, Jeodezi ve Cografi Bilgi Teknolojileri E.A.B.D.
Tez Yöneticisi: Doç. Dr. H. Şebnem DÜZGÜN
Nisan 2007, 113 sayfa
Arama kurtarma operasyonları, kayıp nesneleri belirli bir alanda en kısa sürede ve
en uygun kaynak kullanarak bulmayı hedefler. Bu operasyonlarda temelde iki
sorun ile karşılaşılır. Birincisi, güvenilirliği yüksek bir olasılık haritası elde etmek;
ikincisi ise, üretilen olasılık dağılımına göre belirlenen bölgeyi, en kısa sürede
havadan taramada kullanılabilecek arama desenini belirlemektir. Yüksek
güvenilirliğe sahip olasılık haritaları üretmek, taranacak alanın küçültülmesi ve
tarama süresinin azalması için gereklidir.
Bu çalışmada, kayıp nesnelerin en kısa sürede bulunması ve en uygun kaynak
kullanımına altlık sağlayacak Coğrafi Bilgi Sistemleri (CBS) tabanlı Çok Ölçütlü
Karar Verme Analizi (ÇÖKVA) ile bütünleştirilmiş arama teorisine dayalı bir
model geliştirilmiştir. Geliştirilen model, arama kurtarma uzmanlarının CBS
ortamında kullanabileceği bir arama kurtarma planlama yazılımı olarak
kodlanmıştır. Model girdisi olarak kaybolan nesnenin en son görülme koordinatları
ve ilintili diğer bilgiler kullanılmaktadır.
vii
Modelde öncelikle ilgili katmanlar uzman görüşüne göre öznel olarak
önceliklerine göre sıralanır. Ardından uygun olan ÇÖKVA metodu seçilir ve her
katman uzmanın verdiği ağırlığa göre sıralanır. Olasılık haritası ÇÖKVA
metodunun çıktılarına göre belirlenir. İkinci basamakta ise oluşturulan olasılık
haritası temel alınarak literatürde kullanılan arama teorisine dayalı metotlar
kullanılarak en uygun arama metodunun deseni belirlenir. Geliştirilen model ve
yazılım arama kurtarma operasyonlarındaki arama süreçlerini kısaltması ve farklı
kazaların verilerine göre en uygun arama desenini bulması bakımından alanda yeni
bir yaklaşımdır.
Anahtar Kelimeler: Arama Kurtarma, Çok ölçütlü karar analizi, CBS, Arama
Teorisi, Kütahya
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To My Love
And
To My Family
ix
ACKNOWLEDGEMENTS
First and foremost, I would like to express my gratefulness for Assoc. Prof. Dr. H.
Şebnem Düzgün regarding her major contributions, her unlimited patience and the
valuable assistance and orientation she has provided. Her crucial and effective
interventions during the whole study have provided me great ease at handling the
problems that I have confronted.
I am grateful to my instructors Assoc. Prof Dr. Nurünnisa Usul, Assist. Prof. Dr.
Zuhal Akyürek, Prof. Dr. Vedat Toprak, and Assoc. Prof. Dr. Oğuz Işık, for their
constant support and enlightening vision that helped me out many times
throughout every stage of the study.
I would like to thank to GGIT assistants Pınar Aslantaş Bostan, Serkan Kemeç and
the personnel of Turkish Air Forces; Major Ali Gerede and Turgut Varol. I would
like to thank all those people who have made this thesis possible. This work has
been possible with the help of many other people. Especially my manager, Özen
Abanoz, who has believed in this thesis more than me and also my friends who
help in this process, Buket Cicioğlu, Sinan Pınarbaşı and my manager, Bülent
Üncü. Also I would like to thank to my colleagues, İlker Akbay, Barış Kirtil,
Şengül Sur Ertunç, İnönü Abacı and Esra İnce.
I would like to thank to my dear friends Erol Aran, Başak Yılmaz, Ender Özmen,
Mustafa Öger my friends Serbülent Öcal and Çağatay Yamak for supporting me,
helping me and distracting me, when I was working on my thesis.
Finally, to my wife Aycan, if words could only describe all the ways you have
saved me since we met. We have grown, loved, cried, fought, and made peace
together. You are my emotional center and my other half. Thank you for being
“truly yourself”
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TABLE OF CONTENTS
ABSTRACT...................................................................................................... iv
ÖZ..................................................................................................................... vi
DEDICATION ............................................................................................... viii
ACKNOWLEDGEMENTS ............................................................................. ix
TABLE OF CONTENTS ...................................................................................x
LIST OF FIGURES ........................................................................................xiv
LIST OF TABLES ........................................................................................ xvii
CHAPTER
1 INTRODUCTION ......................................................................................1
1.1. Search Theory in SAR .........................................................................3
1.2. Problem Definition and Aim ................................................................6
1.3. Organization of the Thesis..................................................................11
2 SEARCH THEORY AND SAR ...............................................................13
2.1. Principles of Search Theory ...............................................................13
2.2. Search Sequence ................................................................................16
2.2.1. Estimating the Last Known Position (LKP)...............................16
2.2.2. Definition of the search area .....................................................17
2.2.3. Selecting Appropriate Search Pattern ........................................18
2.2.4. Determining the Desired Area Coverage ...................................22
2.2.5. Developing an Optimum Search Plan........................................23
2.3. Probability Density Distribution and Probability Maps.......................24
2.4. Determining a search area ..................................................................24
3 FRAMEWORK OF DEVELOPED METHODOLOGY ........................26
3.1. Data collection ...................................................................................28
3.2. Probability Maps................................................................................28
xi
3.2.1. Simple Additive Weighting (SAW)...........................................31
3.2.2. Analytical Hierarchical Process (AHP) .....................................32
3.2.3. Ordered Weighted Averaging (OWA).......................................35
3.3. Classification of probability map........................................................38
3.4. Determining a suitable Search Pattern ................................................39
4 SOFTWARE DESIGN AND DEVELOPMENT.....................................40
4.1. Introduction .......................................................................................40
4.2. Requirements .....................................................................................40
4.3. Development Environment.................................................................41
4.4. MCDA-based Probability mapping ....................................................41
4.5. Probability of Area (POA) based segmenting .....................................43
4.6. Search pattern comparison .................................................................44
4.7. User Interface.....................................................................................47
4.8. Modules of the tool ............................................................................47
4.8.1. The MCDA Module..................................................................49
4.8.1.1 Simple Additive Weighted.............................................49
4.8.1.2 Analytical Hierarchical Process .....................................50
4.8.1.3 Ordered Weighted Averaging ........................................51
4.8.2. The Classification Module ........................................................52
4.8.3. Pattern Comparison Module......................................................53
5 IMPLIMANTATION...............................................................................56
5.1. Case Study Area.................................................................................56
5.2. Data Collection and Preparing Data Layers ........................................58
5.2.1. Signal Points.............................................................................64
5.2.2. Settlement Layer.......................................................................65
5.2.3. Roads Layer..............................................................................66
5.3. Construction of Probability Maps based on MCDA............................67
5.3.1. Simple Additive Weight (SAW)................................................67
5.3.2. Analytic Hierarchy Process (AHP)............................................69
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5.3.3. Ordered Weighted Averaging (OWA).......................................71
5.4. Reclassification of the Probability map ..............................................73
5.4.1. Reclassification According to Result of SAW...........................74
5.4.2. Reclassification According to Result of AHP............................78
5.4.3. Reclassification According to Result of OWA ..........................81
5.4.4. Dividing area into grids.............................................................85
5.5. Discussions ........................................................................................86
6 CONCLUSION AND RECOMMENDATIONS .....................................92
6.1. Conclusion.........................................................................................92
6.2. Recommendations..............................................................................94
REFERENCES..................................................................................................95
APPENDICES
A. VARIABLES FOR SPECIFIC SEARCH TYPES .............................100
B.1 OVERVIEW ....................................................................................104
B.2 MAP ALGEBRA CLASS FOR AHP ..............................................104
B.3 MAP ALGEBRA CLASS FOR SAW ..............................................108
B.4 POA SEGMENT MODULE.............................................................110
B.5 SEARCH PATTERN COMPARISON MODULE............................111
C. ASPECT VARIATIONS OF THE AREA .........................................113
xiii
LIST OF TABLES Table 3-1 Saaty’s fundamental scale....................................................................35
Table 5-1 Structure of graphical data layers.........................................................60
Table 5-2 Structure of non-graphical data layers..................................................61
Table 5-3 SAW ranking .....................................................................................68
Table 5-4 AHP method criteria............................................................................70
Table 5-5 Preferences of Search Expert according to OWA method ....................71
Table A-1 Search Types and Altitudes (IAMSAR, 2001) ..................................100
Table A-2 Sweep width for visual searches (km/nm) (IAMSAR, 2001).............101
Table A-3 Search correction factors (IAMSAR, 2001) ......................................101
Table A-4 Recommended search altitudes (IAMSAR, 2001).............................101
Table A-5 Sweep width table ............................................................................102
xiv
LIST OF FIGURES
FIGURES
Figure 2-1 Coverage vs. POD.............................................................................14
Figure 2-2 Track Crawl Search Pattern (NSAR, 1998) ........................................18
Figure 2-3 Parallel Track Search Pattern (NSAR, 1998) ......................................19
Figure 2-4 Creeping Line Search Pattern (NSAR, 1998)......................................20
Figure 2-5 Expanding Square Search Pattern (NSAR, 1998) ...............................20
Figure 2-6 Sector Search Pattern (NSAR, 1998)..................................................21
Figure 2-7 Contour Search Pattern (NSAR, 1998) ...............................................22
Figure 2-8 Sweep Width (NSAR, 1998) ..............................................................23
Figure 3-1 Stages of Developed Methodology.....................................................27
Figure 3-2 Layer structure ...................................................................................28
Figure 3-3 Lateral Range Curve (IMCO, 1980) ...................................................29
Figure 3-4 Matrix A ............................................................................................33
Figure 3-5 Decision flowchart for MCDA of Malczewski (1999) adapted for SAR
operations ...........................................................................................................38
Figure 4-1 MCDA based probability mapping module ........................................42
Figure 4-2 POA segment module ........................................................................43
Figure 4-3 Search pattern comparison module.....................................................44
Figure 4-4 Attribute table of Probability map grid ...............................................45
Figure 4-5 Relation between selected cell and whole probability map .................46
Figure 4-6 According to POA search area can be divided into sub sectors. ..........46
Figure 4-7 Main menu of the METUSAR Tool. ..................................................47
Figure 4-8 MCDA menu view.............................................................................48
Figure 4-9 Classification menu............................................................................48
Figure 4-10 Pattern selection menu .....................................................................48
Figure 4-11 Layer available in open application ..................................................50
Figure 4-12 AHP form ........................................................................................51
xv
Figure 4-13 OWA form.......................................................................................52
Figure 4-14 Reclassify form ................................................................................53
Figure 4-15 Grid making form.............................................................................54
Figure 4-16 Pattern comparison form ..................................................................55
Figure 5-1 Geographical location of the study area..............................................57
Figure 5-2 Digital elevation Model Layer and Map layer of the area ...................58
Figure 5-3 Framework of developed methodology ..............................................59
Figure 5-4 Range of the air plane ........................................................................62
Figure 5-5 Areas with certain probabilities around last known position ...............63
Figure 5-6 Location of signal points ....................................................................64
Figure 5-7 Visibility of Settlement layer of the study area ...................................66
Figure 5-8 Intersection of visibility map and settlement buffers...........................66
Figure 5-9 Visibility map from the view of roads ................................................67
Figure 5-10 Intersection of visibility map and road buffers..................................67
Figure 5-11 Probability map produced with SAW method...................................69
Figure 5-12 Probability map produced from the AHP method .............................70
Figure 5-13 Probability map produced from the OWA method............................72
Figure 5-14 SAW classification with 3 classes ....................................................74
Figure 5-15 SAW classification with 4 classes ....................................................75
Figure 5-16 SAW classification with 5 classes ....................................................76
Figure 5-17 SAW classification with 6 classes ...................................................76
Figure 5-18 SAW classification with 7 classes ...................................................76
Figure 5-19 SAW classification with 8 classes ....................................................77
Figure 5-20 SAW classification with 9 classes ....................................................77
Figure 5-21 AHP classification with 3 classes ....................................................78
Figure 5-22 AHP classification with 4 classes .....................................................79
Figure 5-23 AHP classification with 5 classes .....................................................79
Figure 5-24 AHP classification with 6 classes .....................................................80
Figure 5-25 AHP classification with 7 classes .....................................................80
Figure 5-26 AHP classification with 9 classes .....................................................81
xvi
Figure 5-27 OWA classification with 3 classes....................................................81
Figure 5-28 OWA classification with 4 classes....................................................82
Figure 5-29 OWA classification with 5 classes...................................................82
Figure 5-30 OWA classification with 6 classes....................................................83
Figure 5-31 OWA classification with 7 classes....................................................83
Figure 5-32 OWA classification with 8 classes....................................................84
Figure 5-33 OWA classification with 9 classes....................................................84
Figure 5-34 MCDA and Search Pattern Comparison ...........................................87
Figure 5-35 Search Efforts (Z) for SAW..............................................................88
Figure 5-36 Search Efforts (Z) for AHP ..............................................................89
Figure 5-37 Search Effort (Z) for OWA ..............................................................89
Figure 5-38 The result of comparing three decision rules.....................................90
Figure C-1 Aspect Variations of the area...........................................................113
1
CHAPTER 1
1 INTRODUCTION
Search and Rescue (SAR) is an operation to find and rescue the people in distress
either in a difficult area, such as in mountains, deserts, forest or at sea (Stone,
1975). SAR operations have several distinct stages. In the first stage, the most
likely location of the missing object is determined. This information is then
processed with other considerations to decide the initial importance and scale of
the operation (Frost, 1999).
The next stage is the search stage, in which a search is mounted by appropriate
search tools like searching the area from air or land. When a search mission is
required, there are four factors which should be considered immediately:
1) An adequate description of the search target,
2) The search area condition, including weather and any possible risks and
dangers,
3) The best search pattern, sweeps the area optimally,
4) The appropriate track spacing, according to the terrain conditions (Haley
and Stone, 1979).
Then the third stage is the Rescue stage, at this stage support is rendered to the
object where it is found, to allow it to be safely transported to a place, where more
intensive aid can be provided.
Finding the people in distress as quickly as possible requires well designed SAR
planning and better use of technology (Zeid and Frost, 2004). For either complex
or simple search operations, a search plan should always be developed by the
2
control of the search expert, as many lives may depend on the care with which the
search is planned and conducted (NSAR, 1998).
SAR planning operations differ from each other according to the search location.
These are; inland search and rescue planning, maritime search and rescue
planning, and aeronautical search and rescue planning. The latter is the subject of
this study.
Aeronautical Search and Rescue (ASAR) term defines operations that are carried
out by aircrafts which are the most satisfactory units for searching large areas
quickly (IMCO, 1980). An ASAR planning operation characteristically involves
three main parts, and requires several layers of information about probable
position of the target (IAMSAR, 2001). The first and second steps in aeronautical
search planning are to search along the track visually and electronically and
determine the limits of the area containing all possible survivor locations,
respectively (NSAR, 1998). This is usually done by determining the planned route
of the missing airplane. Currently, search planners are mainly using the New Two-
Area Method (NTAM) which sweeps the either side of track the search area along
the route of the plane; commonly a search performed in an area of 10 nautical
miles either side of track, the method was developed by the Canadian Department
of National Defense’s Directorate of Air Operational Research (Zeid and Frost,
2004). This method is based on research of 76 missing aircraft missions conducted
in Canada from 1981 to 1986 (NSAR, 1998). The usage of this method requires
search planners to have the last known position (LKP) of the missing aircraft and
the intended route of the missing aircraft, and the intended destination of the
missing aircraft. From this information the search area is defined for prioritizing
the search.
3
The last stage in ASAR planning is computation of a probability area by using
navigational tolerance, LKP and signals from the area. Hence, SAR planners have
to know the LKP, route, and destination as discussed, but there are other
mitigating factors that often allow planners to focus the search efforts. These
include a known flight plan, signals from the area, radar, witnesses, or known
Emergency Locator Transmitters (ELT) signals or distress calls in the area of
possibility of the search.
1.1. Search Theory in SAR
The theory of how to search for missing objects has been a subject of serious
scientific research for more than 50 years. It is a branch of the broader applied
science known as operations research (Frost, 1999). In more recent years, the
principles of operations research have been applied to a wide variety of problems
that involve making good decisions in the face of uncertainty about many of the
variables involved (Champagne et al., 1999).
Search theory is defined by Cadre and Soiris (2000) as a discipline that treats the
problem of how a missing object can be searched optimally, when the amount of
searching time is limited and only probabilities of the possible position of the
missing object are given. The development of search theory and its search
application and rescue consist of three main parts. These are scientific research and
subsequent developments, developments of search planning doctrine with SAR
manuals and development of computer based search planning decision support
tools (Stone, 1989).
The first part is mainly developments in a scientific research side. Search theory
was first established by Koopman during World War II using the new techniques
of operations research. First applications of search theory were made on military
4
operations (Stone, 1975). Koopman (1980) stated that the principles of search
theory could be applied effectively to any situation where the objective is to find a
person or object contained in some restricted geographic area. After military
applications it was applied to different problems such as; surveillance,
explorations, medicine, industry and search and rescue operations (Haley and
Stone, 1979). The aim of searching in the context of ASAR is to find the missing
aircraft effectively and as quickly as possible with the available resources (Stone
1989). In search theory framework, effectively means minimizing the time
required to find the search object while maximizing the chances for finding the
object.
Koopman (1956) defined Search theory in three reports. The first one is
Kinematics-based, which includes the analytical description of equations of a
target and observer movement, description of equations of probability value of
connecting an observer and a target and equation describing the randomly
distributed targets. The second report was about target detection that consisted of
analytical description of instantaneous probability for target location, analytical
description of horizontal distance distribution and the analytical description for a
common case of a random search. The last report investigated the problem of
optimum distribution of search efforts.
The second part of developments in search theory involves establishment of search
planning doctrine. In 1957 the U.S coast guard first articulated its search planning
doctrine in the form of search and rescue manual and showed how the basic
principles of search theory were applied to the SAR planning process. (Stone,
1989)
The third part is development of computer based search planning decision support
tools. According to Stone (1989) in the early 1970s Richardson (1972) developed
5
the computer assisted search planning (CASP) system for dynamic planning of
search for ships and people lost in the sea. Then, CASP systems were developed to
assist U.S navy in planning submarine searches.
The first computer based systems mostly used for marine SAR operations (Stone
1975). In order to reach a target in minimum time with limited resources, it is
important to use CASP systems to increase the speed of the search. With the
development of search theory CASP systems have been used since 1970s.
CASP systems were first introduced by United States Coast Guard in 1974s
(Champagne et al., 1999). It was based on Monte Carlo simulation and applied in
naval SAR operations. CASP generates an initial probability distribution taking in
to account current wind and environmental information. Richardson and Corvin
(1980) used CASP systems in marine SAR operations. They have stated three
types of SAR scenarios to construct initial probability map. These are referred as
position, area and track line scenarios for distress objects. Position Type initial
target probability distribution is modeled as bell shaped distribution because it
considers that the missing object is not stationary. In the area type scenario the
search area is bounded and the probability in the area is thought to be as uniform
for distress object. The third scenario is the track line scenario. It is used if a
reported track of the object is assumed to be true.
CASP systems were limited in terms of spatial data (Cooper et al., 1999).
Therefore, in order to put up limitations about spatial data, it is important to
integrate GIS into CASP systems used in ASAR operations. Use of GIS is highly
increased in SAR operations. Hence, GIS permits to analyze the relationships
between different data layers easily and effectively.
6
Cooper et al., (1999) have focused inland search and planning techniques. They
developed a methodology for land search planning and developed computer based
search planning decision support tool for land SAR. Champagne et al., (1999)
tested three of the search patterns used only for naval search and rescue operations.
They examined with respect to number of U boats sighted by aircrafts as a
measure of search efficiency. Their studies conclude that search patterns have
impacts on search efficiency in naval SAR operations.
Wollan (2004) investigated creation of search patterns in ASAR operations.
Besides generic search patterns in use; Wollan tested heuristic algorithms in the
mean of minimizing the time. In the study of Wollan (2004) added GIS into a
developed search management implementation. Also Wollan offered to modify
search patterns individually to accommodate the area that needs to be searched
instead of using generic search pattern.
Zeid and Frost (2004) have developed a decision support systematic for Canadian
search and rescue operations in the case of lost air craft. They developed an
optimization module based on search theory, on gradient search methods. They
compared their system with current Canadian manual SAR system.
1.2. Problem Definition and Aim
Search and rescue operations are spatial activities (Haley and Stone, 1979). Search
planners must combine information on where the missing object was last seen,
likely routes, and maps of the areas already searched, time last searched, and
available resources to effectively mount a search area (Burrough and Frank, 1995).
The main problem is to produce the reliable probability maps, which accounts for
these clues (Stone, 1975).
7
The majority of the SAR planning is made through the ease of methods described
in the United States National Search and Rescue Manual (NSM) and Canadian
National Search and Rescue Manual (NSAR). However, these documents are
adequate for describing search planning doctrine and providing practical guidance
for planning searches with only traditional tools like pencil, paper, nautical charts
etc. They are inadequate as a guide to search theory or to the practical application
of the currently available considerable computing power to the search planning
problem (Frost, 1999).
While preparing probability maps of the suspicious distress area, integrated spatial
technologies such as geographic information systems (GIS) would be an ideal
solution for aeronautical search and rescue operations (USADT, 2001). The
dynamic relation between maps and the spaces represented in SAR operations is
common to geographic information systems. Therefore, GIS presents itself as the
most useful tool in making effective SAR operations.
Moreover, for more accurate probability maps it is important to include more
inputs about the location of the missing object (USR, 1991). In order to enrich the
information about the area and getting more reliable probability maps about its
distribution on the area, GIS could be used as a tool (Armstrong and Cook, 1979).
In the absence of inputs on the contrary, it may be assumed that the most probable
area within which a missing aircraft will be found is along the intended track from
LKP to intended destination and within a reasonable distance either side of track
(NSAR, 1998).
Each SAR mission has different characteristics (Stone, 1983). Zeid and Frost
(2004) stated that, parameters related with the missing plane, such as intended
route, is an important role of the SAR planning. However, if the route of the plane
is not known like air combat maneuvering, achieving this step cannot be possible
8
to sweep the route of the plane. In this kind of search operations it is impossible to
sweep the either side of track, in the search area along the route of the plane. It can
be faced with many crashes, such as dog fight flights which is a common flight
type used to describe close-range aerial combat between military aircrafts (Web 1).
In this type of flights route information of the missing plane cannot be determined
to include into SAR mission planning. SAR planners can cope with this problem
by calculating maximum distance the survivors could have traveled between the
time of their LKP and the known or assumed time of the distress incident and
drawing a circle of that radius around the LKP. Knowing the extreme limits of
possible locations allows the search planner to determine where to seek further
information related to the missing airplane. However, systematic search of such a
large area is normally not practical. Therefore, the next step is shrinking the search
area as possible.
Besides the problem of decreasing size of the search area, an aeronautical search
and rescue operation requires access to information from many different sources in
order to properly respond to an emergency or incident. An incident must be
understood within the context of the environment that it has occurred (Robe and
Frost, 2002). Generally search environment is very dynamic and SAR operations
need to include all of the dynamic factors. Therefore this process can be
summarized as location of an incident and how to access that location. The most
logical way of organizing such data could be geospatial monitoring and analysis.
Missing aircraft search methods are very intensive and tie up many resources that
could be used elsewhere (Stone, 1975). SAR planning procedures plans basically
where, when and how to search. Therefore, determining the optimal search area is
the main problem. The subject of search theory is constructing a probability
distribution for the location of the missing object in order for optimal resource
allocation (Cadre and Soiris, 2000).
9
The use of GIS in SAR operations is growing very rapidly. SAR operations benefit
greatly from the GIS technology in recent years. In recent studies (Liu et al., 2006;
Wollan, 2004; Zeid and Frost, 2004) lots of applications were developed to help
solving SAR problems. Many early systems like computer aided search planning
systems (CASP) were developed to solve relatively narrow, specific kinds of
problems. The past twenty years have seen an explosion in the technological base
for these systems, particularly in the areas of spatial data processing in GIS
technologies. Zeid and Frost (2004) tested how their GIS integrated tools could be
applied in SAR operations in Canada. Wollan (2004) used GIS for defining the
search area, generating the search patterns and viewing the current status of search
effort. (Web 2) presented examples of how the development of GIS increases
capabilities in a natural disaster management in the example of SAR operations in
India.
According to above mentioned state of the art, the following problems related to
SAR operations can be listed:
1) The problem of producing highly reliable probability map from the limited
available information to conduct a SAR operation,
2) Highly subjective decisions by SAR expert while distributing resources to
the area,
3) Dividing the area into sub sectors before considering the probability map,
4) The problem of having no information about intended route, and intended
destination of the missing aircraft.
In the present context, due to scientific advances it has become easier to carry out
SAR operations efficiently with the use of GIS, which help to identify areas that
are probable location of missing objects, searching them according to probability
10
distribution maps, and simulating search operation according to those maps.
Moreover, GIS is useful even in managing SAR planning as it provides instant
access to information and analyzing efficiently required for search management
decisions (Web 3).
In this study it is aimed to develop a new systematic integrated methodology
which could reduce the time it takes to find survivors of plane crashes, and thus
save lives. The proposed methodology with the desired characteristics integrates
Search Theory, for constructing reliable probability distribution maps with GIS;
for acquiring, integrating and analyzing data coming from different heterogeneous
sources and Multi Criteria Decision Analysis (MCDA) methods for constructing
probability maps.
Particularly, this study focuses on incorporating the spatial data and clues from
variety of sources to create a meaningful, accurate and comprehensive
representation for aeronautical search and rescue planning. After preparing reliable
and accurate probability map, search pattern efficiency is tested. In order to do this
properly, the criteria necessary for the preparing of the probability map is
reviewed.
The integration of GIS and MCDA provide a powerful tool to generate probability
maps of the search area, since GIS provides efficient manipulation and
presentation of the spatial data and MCDA supplies consistent ranking of the
spatial layers and clues from the area based on a variety of criteria.
The proposed methodology is coded as computer software (METUSAR), which
allows SAR planners to observe how the conditions and clues affect the
probability maps and SAR planning process. It creates a condition for SAR
planners for ranking and rating the environmental and geographical data. In
11
computer program three goals are met. Firstly, the system is designed in a manner
that allows for ease of understanding for the search experts. Secondly, the system
involves all the steps of preparing probability map for SAR operations rather than
separate parts of functions. Thirdly, the system contains spatial and geographical
data, in order to get more accurate probability map. These characteristics are
combined in geographic information systems, based on multi-criteria decision
analysis tool for SAR operations.
The developed methodology and the coded Software are implemented on the case
of Plane Crash in Kutahya in 2004. The search time of this crash was one of the
most excessive ones in the Turkish ASAR (Gerede, 2007), as the plane was in a
dog fight. The case is used for performance testing of the methodology and the
software.
The main innovation of this study is integration of search theory and MCDA
within GIS framework and providing a single integrated system for ASAR
operations. The proposed methodology provides easy retrieval of spatial and non
spatial information, analysis of this information in the light of Search Theory
concepts and estimating consequences of proposed SAR plans.
1.3. Organization of the Thesis
Outline of the thesis is as follows;
First chapter starts with a brief summary of SAR planning and problem definitions
as well as aim of the thesis.
In the second chapter, the historical and theoretical framework of Search Theory is
presented. The terminology is defined briefly.
12
In the third chapter, developed methodology of the thesis is explained. Also,
preparing the probability map with using MCDA is described. Moreover, the steps
of methodology which are classification of probability maps and search pattern
comparison are discussed.
In the fourth chapter, the software design and development is mentioned, module-
by-module.
In the fifth chapter, implementation of application on the case study area is
discussed. The data related to the incident is presented and the status of the
incident area is set accordingly. Geographical settings of the study area and all
layers used are given. Different search patterns are compared.
In the Conclusion chapter, an evaluation is made, regarding the aim, objective of
the study and analysis for these objectives. Finally, recommendations and
conclusion of the study were discussed and some ideas are given about the future
studies.
13
CHAPTER 2
2 SEARCH THEORY AND SAR
2.1. Principles of Search Theory
One of the goals of search theory is to provide optimal solutions in the Search and
Rescue (SAR) context. A search plan would be optimal if it provides maximum
probability of success (POS), which is the probability of finding the object (Zeid
and Frost, 2004). Two concepts are also important: the probability of area (POA)
which is the probability that the search object is contained within the boundaries of
a region, segment, or other geographic area and the probability of detection (POD)
which is probability of detecting the search object in the determined area. The
relation between POD, POA and POS is given in Equation 2.1
POAPODPOS ×= (2.1)
The POD of a search is determined by the coverage, as shown in Figure 2-1.
Coverage (C) is the ratio of the search effort (Z) to the area searched (A) and is
equal to Z/A. For parallel sweep searches where the searcher tracks are perfectly
straight, parallel, and equally spaced, it may be computed as the ratio of effective
sweep width (W) to track spacing (S) or C = W/S. "A" (area searched) and "Z"
(search effort) must be described in the same units of area. W (effective sweep
width) and "S" (track spacing) must be expressed in the same units of length.
Coverage may be thought of as a measure of thoroughness (NSAR, 1998). The
POD may be derived from the POD vs. Coverage graph (Figure 2-1).
14
Figure 2-1 Coverage vs. POD
Effective sweep width is a measure of detectability. Effective sweep width
depends on the search object, the sensor, and the environmental conditions existing
at the time and place of the search. Real effective sweep width values must be
measured via exact scientific experiments, but rationally accurate estimates may be
made from tables of effective sweep widths (Tables A-1, A-2, A-3 and A-4) that
have been experimentally determined for various search situations. A less accurate
method of estimation for visual search is to assume the effective sweep width
equals to the visual distance.
The effective sweep width may be thought of as the width of a swath centered on
the sensor’s track such that the probability of failing to detect an object within that
width equals to the probability of detecting the same object if it lies outside that
width, assuming the object is equally likely to be anywhere (Frost, 1999).
15
Another, equivalent, definition is: If a searcher passes through a swarm of identical
stationary objects uniformly distributed over a large area, then the effective search
width, W, is defined by the Equation 2.2,
sVN
NW
×=
2
1 (2.2)
Where Vs is searcher speed, N1 is Number of objects detected per unit time and N2
is Number of objects detected per unit area. All values in Equation 2.2 are
averages over a statistically significant sampling period. Generally, a significant
increase in search speed will decrease the effective sweep width. W is needed to
compute the search effort (Z), and Z is needed to compute the C based on the
amount of search effort expended in the segment relative to the segment’s physical
area.
The effective sweep width (W) times search speed (V) times hours spent in the
search area (T) equals to the search effort (Equation 2.3) for one searcher or one
resource.
TVWZ ××= (2.3)
Alternately, Z = W x D, where D is the linear distance traveled. The unit of
measure for search effort is described in area. If multiple searchers simultaneously
follow independent paths when searching and together achieve approximately
uniform coverage of the segment, then the total search effort is given by Equation
2.4
16
)( TVWnZ ×××= (2.4)
Where n is the number of searchers.
2.2. Search Sequence
Generally five steps for search and rescue operations are adequate. These steps are
as follows: (NSAR, 1998)
1. Estimating the Last Known Position (LKP)
2. Determining the size of the search area
3. Selecting appropriate search patterns
4. Determining the desired area coverage
5. Developing an optimal search plan
2.2.1. Estimating the Last Known Position (LKP)
The term Last Known Position (LKP) expresses the last witnessed, reported, or
computed position of a lost object. Estimating the LKP is essential for determining
the probable position of the target in ASAR cases (IMCO, 1980; IAMSAR, 2001).
According to Thomas and Hulme (1997) as randomization of the initial position of
a stationary target is equally likely to be at any point in the area, besides LKP,
every clue about target should be considered before preparing probability maps.
Estimating the LKP of the target is the first step of an efficient search planning
process (Champagne et al., 1999). Therefore, getting clues about the target,
determining where it was last seen, and defining the approximate size and location
of the area, where the subject could be are all important preliminary steps for any
kind of search problem.
17
2.2.2. Definition of the search area
The second step of the SAR planning is highly related with LKP and probability
map. According to NSAR (1998) the search area is the section that bounded by the
target’s limit of endurance in all possible directions from the LKP of the search
object. It approximates a circle centered on the LKP with the radius being
expressed in terms of distance.
After the search area has been determined, the next step is to assign a probability
map location of the search object, which is called POA. According to NSAR
(1998), estimated location for a stationary object is based on one of the following
three distributions: point datum, line datum or area datum. The point datum is a
bivariate normal distribution centered on the LKP with no correlation between x
and y variables. The standard deviations for these variables assumed to be equal.
This standard deviation represents the error in the estimation of the last known
position. Position error may also be expressed as the total probable error of
position, E. This quantity represents the radius of the circle around the LKP that
contains 50 % cumulative probability that the search object located with in the
circle. The line datum is used when both LKP and destination are known. It
corresponds to a uniform distribution along the flight path. The area datum is
uniform distribution that is appropriate when very little is known about flight plan
of the search object.
Normally, it is impractical to search wide area; hence, searching the whole area is
not optimal. In the absence of information, it may be assumed that the most
probable area that missing aircraft is found is that along the intended track from
the last known position to intended destination.
18
2.2.3. Selecting Appropriate Search Pattern
There are mainly six search patterns according to the National Search and Rescue
Manual for visual searches from the air:
1. Track crawl search pattern
2. Parallel track search pattern
3. Creeping line search pattern
4. Expanding square search pattern
5. Sector search pattern
6. Contour search pattern
Track Crawl Search Pattern:
The Track crawl pattern is usually used as the initial search action, and is based on
the assumption that the search object’s route is known. Track crawl pattern (Figure
2-2) can be used on electronic or visual searches if the route of the target is known
before the crash time.
Figure 2-2 Track Crawl Search Pattern (NSAR, 1998)
19
Parallel track search pattern:
Parallel Track search pattern (Figure 2-3) which is employed to provide uniform
coverage over the search area is used for the successive search legs advancing
across a search area.
Figure 2-3 Parallel Track Search Pattern (NSAR, 1998)
Creeping Line Search Pattern
Creeping Line Search Pattern (Figure 2-4) like Parallel Track pattern is employed
to provide uniform coverage over areas where only the approximate position of the
target can be estimated. The difference between parallel track and Creeping line
search pattern is if legs are parallel to the shortest side of the search area it is called
Creeping line search pattern.
20
Figure 2-4 Creeping Line Search Pattern (NSAR, 1998)
Expanding Square Search Pattern:
The Expanding Square Search Pattern (Figure 2-5) is used when the location of the
stationary search object is known with reasonable accuracy. If first coverage of an
area is not adequate, the tracks should be angled at 45 degrees to the first coverage
for the second coverage.
Figure 2-5 Expanding Square Search Pattern (NSAR, 1998)
21
Sector Search Pattern:
The Sector Search Pattern (Figure 2-6) is used when probability map is established
with a high degree of confidence, the search area is not extensive and the search
object is difficult to detect. The main advantage of a sector search is that track
spacing at the centre of the search is very small, resulting in a greater probability
of detection in the area of greatest probability of whereabouts (NSAR, 1998).
According to (NSAR, 1998) sector search patterns should not have a radius greater
than 18 Kilometers for air searches.
Figure 2-6 Sector Search Pattern (NSAR, 1998)
Contour Search Pattern:
The Contour Search Pattern (Figure 2-7) is used for the terrain searches however it
can be hazardous search procedure, and can only be done when the aircraft used
22
must be suitable for the conditions, highly maneuverable and low speed and small
turning radius with adequate power reserve. The restriction of this pattern is that
only one aircraft can search the area.
Figure 2-7 Contour Search Pattern (NSAR, 1998)
2.2.4. Determining the Desired Area Coverage
The coverage factor is a measure of how systematically an area was swept. It is the
ratio of the area effectively swept divided by the physical size of the area. These
steps include consideration of the factors that affects sweep width, track spacing
and number of sweeps. To make optimum use of probability of detection (POD),
sweep width terminology has been developed (NSAR 1998). Sweep width (W) is a
mathematically expressed measure of detection capability based on target
characteristics, weather and other variables (Figure 2-8). W is obtained by
choosing a value less than the maximum detection range so that scattered targets
that may be detected beyond W are equal in number to those which may be missed
within W (Zeid and Frost 2004).
23
Figure 2-8 Sweep Width (NSAR, 1998)
In general two types of inland search are conducted: initial coverage and intensive
coverage. For initial coverage, track spacing (S) is usually dependent on terrain.
For intensive coverage, S is less than 3,2 km with 1,6 km being the norm
depending on terrain (IMCO, 1980).
Mostly, the coverage factor is based on the subjective judgment of the search
expert and the search planner (Frost, 1999). This value can then be used to assess
the effectiveness of the initial coverage and the requirement of repeated searches
of an area.
2.2.5. Developing an Optimum Search Plan
The optimal search problem is made of three basic elements: the probability
distribution for the target’s location or motion, the detection function and the
constraint on the search effects. The target’s position is uncertain usually, but there
is some information about its location. For search theory, it is assumed that the
information has been quantified in the form of a probability distribution function
called the target distribution (Liu et al., 2006).
According to Stone (1975) the detection function relates the amount of search
efforts utilized in searching an area to the probability of detecting the target given
24
that it is located in the area. Usually, the searcher has a limited amount of search
resources available to carry out the search. For each allocation of total search
effort to the various regions of search space, the probability of detecting the target
with that allocation can be computed given the probability distribution function.
Therefore the optimal search problem is to find an allocation of resources which
can maximize the probability of detection, or to minimize the expected
consumption of resources for detecting the target.
2.3. Probability Density Distribution and Probability Maps
Quantifying all the available information about the target’s most probable
locations and establishing the probability density distributions is called a
probability map (Frost, 1999).
Dividing the search area in to the smaller searchable units is named as
segmentation. According to Champagne et al., (1999) segmentation is generally
determined by logistical and operational constraints unrelated to the probability
density distribution on search object location.
Finding probability distribution for a target’s location relies on a subjective
judgment and experience of the search planner. Constructing target location
distribution is strictly related with the last known position of the target and this is a
critical step in preparing a search plan (Stone and Wagner, 1977). According to
Frost (1999) in order to determine starting point, it requires a careful and attentive
assessment of all the available information from all the possible sources.
2.4. Determining a search area
In order to solve a search problem, creating or assuming a probability density
distribution, which represents where the search object is more likely and less
25
likely, is essential (Koopman, 1980). Therefore, determining the border of the
search area is a crucial step in search and rescue operations. When the rough
search area is determined it is necessary to define it to detailed search units. Then
total area is needed to be divided in sub-areas (NSAR, 1998).
26
CHAPTER 3
3 FRAMEWORK OF DEVELOPED METHODOLOGY
The proposed methodology, integrates principles of Search theory through
Geographic Information Systems (GIS) supported with Multi Criteria Decision
Analysis (MCDA) methods. GIS provides a suitable framework for SAR
applications for capturing, storing, retrieving, editing or displaying spatial SAR
data. MCDA provides GIS with the means of performing complex analysis on
multiple and often conflicting objectives, while taking multiple criteria and expert
knowledge into account.
In this study GIS and MCDA analyses are integrated with the ultimate aim of
selecting the most appropriate search pattern, producing probability maps and
segmenting the area according to probability map. In Figure 3.1, the steps of this
methodology are shown..
The methodology has a number of steps (Figure 3-1). The first step is to prepare
the data for generating probability map. In order to do this, digital elevation model
of the area, last known position of the target, location of settlements near the LKP
and roads in the area are used in order to prepare probability maps based on
MCDA methods. Then the probability map is classified. Later different search
patterns are selected and finally the appropriate search pattern is obtained based on
minimum search effort.
27
Figure 3-1 Stages of Developed Methodology
28
3.1. Data collection
Data collection part of Aeronautical Search and Rescue (ASAR) operations consist
of two main parts. Generally, in ASAR cases the most important part is to know
clues about the probable last location of the missing object. The ultimate clue
about the target is last known coordinates (LKP) or its probable route. Altitude and
velocity of the plane before the crash is also important to determine the limits of
the search area (Figure 3-2). The second part is environmental information that
effects construction of probability map and search operation. Vegetation, weather
conditions, size of object, composition of the surface composition of the object
(color, reflective ability), visibility of area from the ground.
Figure 3-2 Layer structure
3.2. Probability Maps
An optimal search plan should include the following procedures; the dimension
and the location of the search area and sub search areas, the assignment of the
available search and rescue units to the corresponding area, along with the track
spaces and altitudes (Cooper et al., 1999).
29
In SAR planning determining the probability of success (POS) is an important
part. POS helps to find the minimum amount of search effort required to achieve
the search process.
POS is determined by probability of area (POA), which is the chance that the
missing object is located within the limits of the determined area. Probability of
detection (POD) is related with the used detection tool, which is the probability of
detecting the target in the given area (Koopman, 1956). Robe and Frost (2002)
described the effective sweep width as a basic measure of how easy or hard it will
be for a searcher to detect the search object under the environmental conditions
that exist at the scene of the search.
The method for estimating effective sweep width is based on concept of a lateral
range curve (Figure 3-3), which refers to the perpendicular distance an object to
the left or right of the searcher’s track where the track passes the object. (Robe and
Frost, 2002) Therefore the distance from the searcher to the object at the closest
point of interaction indicates how narrowly the searcher approaches to the target.
Figure 3-3 Lateral Range Curve (IMCO, 1980)
30
Probability maps consist of regular grids which are geometrically identical square
cells. Each cell is labeled with its POA value. However, the data obtained from
different sources about the possible location of the missing object could be
disordered. Therefore, producing probability maps of the case could be more
difficult. In this case it is important to decide which information is more or less
important. Moreover, different types of information should be combined in
obtaining probability maps. Multi-criteria decision analysis (MCDA) tools provide
a solution to this problem. In this thesis, MCDA methods are used for obtaining
probability maps.
MCDA is a set of procedures to analyze complex decision problems. Main
objective is dividing the problem into small interpretable parts, and then analyzing
every part and integrating them in a logical manner to produce a meaningful
solution for the decision problems (Malczewski, 1999). The types of decision
problems that interface SAR planners typically involve a large set of feasible
alternatives and multiple conflicting evaluation criteria. (Stone, 1989) In order to
solve this kind of problems an appropriate decision rule should be used for overall
assessment (Malczewski, 1999). Since, choosing an appropriate decision rule
according to their performance with respect to evaluation criteria is a main part of
MCDA, results of this process is important for the accuracy of the study.
According to Malczewski (1999) the goal of the MCDA is to choose better
alternatives, to sort alternatives and rank them according to preference. There are
numerous decision rules that can be used for tackling the MCDA problems.
Additive decision rules are the best known and most widely used methods in GIS-
based decision making. The most often used three decision rules: simple additive
weighting (SAW), analytic hierarchy process (AHP) and ordered weighting
averaging (OWA) are discussed below.
31
3.2.1. Simple Additive Weighting (SAW)
Simple additive weighting (SAW) which is also known as scoring methods is a
simple multi attribute decision technique (Malczewski, 1999). SAW method is
based on the weighted average concept which is a direct weight assessment
method. Decision maker assigns importance weights according to his/her
assessments to the variables and makes a numerical scaling of them (Kenneth
1973). An evaluation score is calculated for each alternative by multiplying the
scaled value given to the alternative of that attribute with the weights of relative
importance directly assigned by decision maker followed by summing of the
products for all criteria. The simple additive weighting method evaluates each
alternative, Ai, by Equation 3.1:
∑ ×= jiii xwA (3.1)
Where xij is the score of the ith alternative with respect to the jth attribute, and wj is
the normalized weight, so that∑ =1iw . The weights represent the relative
importance of the attributes (Malczewski, 1999).
The GIS-based SAW methods can be implemented by using GIS having overlay
capabilities. The order of this operation is first defining the set of evaluation
criteria (i.e. GIS layers). Second standardizing each criterion map layer and third
defining the criterion weights according to expert’s choices. After standardizing
the map layers, they are translated to the weighted standardized map layers. GIS
helps generation of the overall score for each alternative using the overlay
operation and ranking of the alternatives according to the overall performance
score (Malczewski, 1999).
32
The main advantage of SAW method is that it is a proportional linear
transformation of the raw data which means that the relative order of magnitude of
the standardized scores remains equal.
3.2.2. Analytical Hierarchical Process (AHP)
The Analytical Hierarchical Process is a technique, developed by Thomas Saaty in
1980 to solve multi-criteria decision problems. It is based on three principles;
decomposition, comparative judgment and synthesis of priorities (Malczewski,
1999).
In AHP process, a decision-maker firstly decomposes the decision problem into
the hierarchical structure typically consisting of three levels. The goal of the
decision is at the top level, the second level consists of the criteria and alternatives
are located in the third level. Then, a simple pair wise comparison is made to
develop the overall weights of criteria for decision making. Finally, the priority of
alternatives is ranked according to the overall scores, which can be synthesized by
considering the weights and values of criteria for each alternative.
In AHP the first step is decomposition, which is called design of hierarchies; in
this stage a complex decision problem is decomposed into a hierarchy with each
level consisting of a few manageable elements by group discussion and group
judgment. In the second step which is weight derivation, relative weights of
decision elements are derived at each level by carrying out pair wise comparisons
for all the elements in each level. The final stage is synthesis level. The overall
priorities are ranked for each alternative according to the scores synthesized by
weights and values of criteria. A final decision can be made based on the ranks
made for each alternative.
33
When the matrix A shown in Figure 3-4 is normalized, the consistency of the
judgment matrix can be determined which is called the consistency ratio (CR)
defined as:
RI
CICR = (3.2)
where CI symbolizes the consistency index and RI the random index. CI is defined as:
)1(
)max(
−
−=
n
nCI
λ (3.3)
Figure 3-4 Matrix A
Where n is the number of items being compared, λ max the average of the values
in matrix. RI is the consistency index of a randomly generated reciprocal matrix
from the 9-point scale, with forced reciprocals (Bottero and Pelia 2005). Saaty
(1990) provided average consistencies (RI values) of randomly generated matrices
(up to 11 × 11 size) for a sample size of 500. In general, a consistency ratio of 10
% or less is considered acceptable. If the value is higher, the judgments are not
reliable and have to be formed again.
34
Two features of the AHP differ from the other decision-making approaches. One is
its ability to handle both tangible and intangible attributes. The other is its ability
to test the consistency according to results of decision maker’s preferences (Roper-
lowe and Sharp, 1990).
It is a quantitative comparison method used to select the optimal alternative by
comparing project alternatives based on their relative performance on the criteria
of interest after accounting for the decision-maker’s relative preference or
weighting of these criteria. AHP wholly aggregates various facets of the decision
problem into a single objective function. The goal is to select the alternative that
results in the greatest value of the objective function. AHP is a compensatory
optimization approach (Saaty, 1990).
AHP uses a quantitative comparison method that is based on pair-wise
comparisons of decision criteria, rather than utility and weighting functions.
Accordingly, it compares the decision elements as to the dominance of one
element over another for each of n elements with respect to an element on the next
higher level using a 1-9 scale (Table 3-1) (Pomerol, 2000).
35
Table 3-1 Saaty’s fundamental scale
Value Definition Explanation
1 Equally important
Two decision elements equally influence the parent decision element
3 Moderately more important
One decision element is moderately more influential than the other
5 Much more important One decision element has more influence than the other
7 Very much more important
One decision element has significantly more influence over the other
9 Extremely more important
The difference between influences of the two decision elements is extremely significant
2, 4, 6, 8 Intermediate judgment values
Judgment values between equally, moderately, much, very much and extremely
All individual criteria must be paired against all others and the results compiled in
matrix form. If the first criterion is strongly more important compared to the
second criterion (i.e. a value of 4), then the second criterion has a value of 1/4
compared to the first criterion. Thus for each comparative score provided, the
shared score is awarded to the opposite relationship. The normalized weight is
calculated for each criterion using the geometric mean of each row in the matrix
divided by the sum of the geometric means of all the criteria. The AHP technique
thus relies on the assumption that humans are more capable of making relative
judgments than absolute judgments.
3.2.3. Ordered Weighted Averaging (OWA)
Ordered Weighted Averaging (OWA) method was first introduced by Yager
(1988). The main difference of this method is to provide a method for aggregating
multiple inputs that lie between the max and min operators. As the term “ordered”
36
implies, the OWA operator pursues a nonlinear aggregation of objects considered.
In the short time since the first appearance, the OWA method has been used in an
amazingly wide range of applications in lots of operational fields. (Byeong, 2006)
OWA decision rule is illustrated by a set of order weights in addition to the
importance weights. With the help of OWA method, the standardized criterion
values aij are multiplied with the corresponding importance weights wj Thus,
ijjik awb ×= (3.4)
Which denote the weighted criterion values for alternative i, but they are re-
ordered so that bi1 > … > bin. Result evaluation scores are calculated as the sum of
the re-ordered standardized criterion values with an additional weighting of the
positions. The score of alternative i is
ikki bvs ×∑= (3.5)
Where vk is the order weight for the k-th position in the re-ordered of sequence
weighted criterion values (Malczewski 1999). The order weights are used to
emphasize the better or the poorer properties of each decision alternative. The set
of order weights is a parameter that determines an instance of the OWA operator.
On the one hand, order weights (1, 0, …, 0) will give full weight to the best
criterion outcome of each alternative, independent of how poorly an alternative
may perform in some other criteria. Alternatives with a single outstanding property
will be ranked highest. This is called an optimistic decision strategy. On the other
hand, order weights (0, …, 0, 1) will give full weight to the poorest criterion
outcome, independent of how well an alternative performs otherwise. Alternatives
with the “least poor” properties will rank highest under this pessimistic decision
strategy. Between these two extremes there is a variety of intermediate strategies,
37
the most important of which is the neutral strategy that does not emphasize any
position in the re-ordered criterion values. The neutral strategy is achieved by
using order weights yields scores that are proportional to those resulting from
simple additive weighting.
n
vk
1= for every k from 1 to n (3.6)
Where vk is the order weight for the k-th position in the re-ordered of sequence
weighted criterion values. Order weights could be defined manually by the user of
an application. Yager (1988) suggests a way of calculating the order weights based
on a parameter α, which corresponds to the decision strategy defines a set of valid
order weights for a given number n of criteria.
aa
kn
k
n
kv )
)1(()(
−−= (3.7)
The α parameter allows a mapping of labels on a qualitative scale to order weights.
As it was discussed, MCDA is a quantitative approach for evaluating decision
problems that involve multiple criteria. The choice of the MCDA decision rule is
very crucial step since it has a significant effect on the final probability distribution
map. MCDA characteristics and properties should be compatible with the specific
nature of the decision problem (Malczewski, 1999).
In order to get appropriate layers, the first step is the standardization of layers. It is
important to get comparable units. The next step is weighting criteria incorporation
of expert preferences, this step is typically carried out by means of relative
importance weights. The last step is aggregating layers according to a specific
38
function. According to a given decision rule, weighted criterion layers are
combined. (Figure 3-5)
Figure 3-5 Decision flowchart for MCDA of Malczewski (1999) adapted for SAR operations
At the end, scores for all input objects in these three MCDA methods were set up
to indicate probability map. The higher MCDA values therefore indicate areas of
higher probability. This map could then be used to divide the search area into sub
sectors. This step should be done by a search expert.
3.3. Classification of probability map
Two striking features of the MCDA analysis results are remarkable: the output
probability maps for every decision rule are numerically and spatially continuous,
which means that it has to be classified subjectively in order to help decision
making. Classification of the resultant probability map provides for search expert
easy understanding of outcomes.
39
In this step, it is also important to determine the number of classes. Less number of
classes generates non interpretable results hence differentiating the probability
map into sub sectors is difficult, on the other hand higher number of classes creates
continual results that are not interpretable. Resulting probability maps contain
many details. Therefore it is important to reclassify these results to more
understandable formats. The suitable number of classes is tested for each MCDA
decision methods.
3.4. Determining a suitable Search Pattern
The details of five search patterns are explained in Section (1). For each of the five
patterns sweep width is determined from the NSAR manual. After deciding the
search area, search patterns are tested on the base grid drawn by SAR tool. Parallel
Track, Creeping Line and Expanding Square searches are used grid searching.
Grid searching for the target via aircraft is the primary technique in aeronautical
Search and rescue operations (O’Conor, 2004). On the other hand Sector Search
Pattern handles the area as a circle. It calculates the radius of the determined
search area.
40
CHAPTER 4
4 SOFTWARE DESIGN AND DEVELOPMENT
4.1. Introduction
The basic aim of this computer program is to integrate Search theory with GIS
through MCDA to be used in Aeronautical Search and Rescue (ASAR) planning
process. In order to do this, the program must provide to the ASAR Planners
interpretable information, but not make the decision for them.
In order to meet described purpose above, the computer program had to be
designed as a tool that would allow SAR planners to prioritize different criteria
and view the results. Furthermore, it had to have the ability to separate the search
area into the sectors by the classification module, which would let SAR planners to
compare and discuss dissimilar results according to the different classification
decisions. This tool also let the SAR planners to compare different search patterns
with their efficiency considering their SAR time.
One of the objectives of this study is testing the search pattern efficiency in
aeronautical search and rescue cases. In order to test search pattern efficiency,
previous steps of the search planning process should be assessed correctly to
coordinate various aspects of the search operation. As a result, developed
implementation has to perform some basic objectives of the search planning steps.
4.2. Requirements
The GIS based system is compulsory for Aeronautical SAR planning process to
handle both vector and raster grid based data well. It should have an easy user
41
interface; one that allows the designer free control on what functions and tools the
user has access too. The system should also function well on a personal computer
and not require a large amount of disk space or computing power.
A concluding requirement of the system is that it must be easily modifiable and
updateable. This is important since the SAR planning process is racing against the
time.
4.3. Development Environment
ESRI’s product ARCGIS which is the most popular GIS program, provides
extending the capability of program by using computer programming languages
like Visual Basic (Web 4). ArcGIS Desktop products give the ability to the user to
develop their own tools. In this thesis a GIS based tool is coded in the form of
Dynamic Link Library (DLL) with ArcGIS 9.1, Visual Basic 6.0 and ArcObjects.
ArcObjects is a desktop GIS application which represents user friendly interfaces
and components to work with GIS functionality. The use of ArcObjects provides a
comprehensive set of components to embed GIS functionality for the SAR
applications. ARC Objects Interfaces are used in the implementation. The
implementation of the software part is divided into three parts:
• MCDA based Probability mapping module
• POA segment module
• Search pattern comparison module
4.4. MCDA-based Probability mapping
Determining the area to be searched and where to start a search operation is the
basic question of the search operations. Starting a search from the high probability
42
areas, which the subject is in, decreases the search time. Probability maps are
determined based on a number of features, to perform the reliable probability
mapping. This tool provides the search expert to test three different multi criteria
decision analysis methods described in Chapter 2. With the help of this tool, search
expert determines the search area according to base probability distribution on the
map. Figure 4-1 and Figure 4-2 represent the results of this process.
Figure 4-1 MCDA based probability mapping module
MCDA based Probability mapping (Figure 4-1) module uses raster, geometry and
map algebra interfaces. MapAlgebraClass calculates inputs from the search expert
and produce a probability map. MapAlgebraClass uses IRaster interface to prepare
raster layers for the necessary algebraic calculations.
For each of MCDA methods, program compares search expert assessment on the
layer-based and produce probability map. Detailed documentation is given in
Appendix B.2.
43
4.5. Probability of Area (POA) based segmenting
Once the probability map of the area has been defined by the software based on the
MCDA methods considering the individual judgments of the search expert, the
next step is to divide the area into segments in order to determine where the search
starts. The tool makes this operation by classifying the probability map with the
help of GIS. The tool also gives the ability to search expert to determine the
number of classes according to the case. It gives the ability to search planner POA
based segmenting the area. Search expert can divide area to sub search areas
(Figure 4-2).
Figure 4-2 POA segment module
POA segment module (Figure 4-2) uses AccessValueOfRasterTable() and
ReclassifyByStringField() classes in order to divide produced probability map into
sub areas visually. AccessValueOfRasterTable reaches pixel value of raster layers.
ReclassifyByStringField() remaps old pixel value of raster layer to user defined
values. Search expert determines the number classes to produce sub sectors from
the probability map. Detailed documentation is given in Appendix B.3.
44
4.6. Search pattern comparison
One of the objectives of this thesis is to test the performance of the search patterns,
and offer the most suitable search pattern for air search and rescue cases. After
dividing area to the segments according to their probabilities, with the help of
ARCGIS’s rectangle tool, the area to be scanned by the aircraft is chosen. The next
step is determining the sweep width by considering the search factor. A search
factor is a variable that is changing according to the features of the terrain. Finally,
the tool compares search patterns according their speed and how quick to sweep
the search area.
Figure 4-3 Search pattern comparison module
Search pattern comparison module has two different functions. First function uses
Segment() classes in order to divide the area into grids. frmGridmaker form get
sweep width and a search factor as input to generate a grid. frmPAtternSelection
form read output of the frmGridmaker form to evaluate the value of search effort
45
for each Search Pattern. Detailed documentation of used functions is given in
Appendix B.4
Figure 4-4 shows the attribute table of the resulted grid shape file. Dimension of
the grid is determined by the search expert considering the sweep width and search
factor.
Figure 4-4 Attribute table of Probability map grid
Search expert can select a specific cell from the produced grid (Figure 4-5). Also,
tool provides two distinct grid shapes in two different shape file formats (Figure
4-6).
46
Figure 4-5 Relation between selected cell and whole probability map
Figure 4-6 According to POA search area can be divided into sub sectors.
47
4.7. User Interface
The most critical factor in the user interface is its ease of use and simplicity. For
ease of use, the focus of the user interface becomes to develop a new Graphical
User Interface (GUI). A GUI is a class of windows that interface between the user
and the underlying program (Burke 2003). The user interface is developed through
Visual Basic 6.0. Main menu is shown in Figure 4-7, with three main modules,
namely MCDA methods, Classify area and select search pattern.
Figure 4-7 Main menu of the METUSAR Tool.
Each GUI have unique pull down menus, pop up menus, buttons and a toolbar. In
ArcGIS, each of the basic system classes (Views, tables, layouts) has their own
GUI. The development of a new GUI allowed the user to leave the complete
ArcGIS capability with the original GUIs (Web 4).
4.8. Modules of the tool
The tool contains three main modules. First module is a MCDA module (Figure
4-8), which gives the chance to the SAR planner applying three MCDA methods
which are Simple Additive Weight (SAW), Ordered Weighted Analysis (OWA)
and Analytical Hierarchical Process (AHP) method.
48
Figure 4-8 MCDA menu view
The second module is Classification module (Figure 4-9) which divides the search
area to the small sectors.
Figure 4-9 Classification menu
The last module (Figure 4-10), which is a Search pattern selection module, gives
ability to the SAR planners by dividing user-defined search area to the grids and
calculating the search time and search effort for the entire search area. This
module also gives the chance to compare the different search methods with
different inputs.
Figure 4-10 Pattern selection menu
49
4.8.1. The MCDA Module
The MCDA module, as mentioned before, is opened by selecting the new ranking
and rating command under the MCDA methods menu of the Tool (Figure 4-8).
This command opens up the three MCDA methods. The MCDA gives the ability
to SAR planners to analyze different type of multi criteria decision analysis
methods like SAW method, OWA method and AHP method.
The MCDA module components firstly list the data layers in the most upper part
of the layers section of the ArcGIS program. It views and collects the entire layer
available there and places them in its list.
4.8.1.1 Simple Additive Weighted
The SAW method which is one of the sub menu of the MCDA menu, (Figure
4-11) queries the user to enter data layer ranking and prioritize them. The program
correctly cancels to script it, when the user fails to enter at least one data layer and
presses the cancel button.
The SAW module next calculates the weights that are to be assigned to each data
layer. It does this by first counting the number of data layers selected and
calculating the distance that each weight must have from the next weight of a base
of one. Finally the weights are normalized so that they add up to one by taking
each weight as a percentage of the sum of all the weights.
50
Figure 4-11 Layer available in open application
4.8.1.2 Analytical Hierarchical Process
The MCDA module performs additive overlay process as it is outlined in Chapter
2. Each layer is multiplied by its weight given by the expert of the SAR planning
and then all the data layers are added together. This result in a grid has values
ranging from zero to hundred and that is the representative of the user’s ranking
priorities. The program then adds this grid theme to the view with the name of
“Grid #”. The number of this name will change each time when a new grid is
created since the program keeps track of an identity number to differentiate
between the output grids.
Firstly program prompts the expert to enter the hierarchy of criteria. Secondly, it
asks the decision maker with pairs of criteria from the hierarchy to judge their
relative importance for them. It works from the bottom of the hierarchy to upwards
rather than from the top down. Hence, expert wants to know how criterion sub-
51
divides before judging it. This process helps to clarify the criterion meaning. After
pair wise comparison process is completed, the program controls the consistency
ratio of the judgment. If it is under 10 %, it prompts the user and does not give
permission to the next step (Figure 4-12).
Figure 4-12 AHP form
4.8.1.3 Ordered Weighted Averaging
The last MCDA module, OWA, uses a fuzzy model. Firstly, program needs to rank
the layer from the most important to the least important one. Secondly, it prompts
the expert to determine the decision strategy: optimistic, moderately optimistic,
neutral, moderately pessimistic or pessimistic.
52
After choosing strategy, the program controls number of layers and it calculates
the ratio according to layer numbers (Figure 4-13). Each layer is multiplied by its
weight given by the OWA decision rule and then all the data layers are added
together.
Figure 4-13 OWA form
4.8.2. The Classification Module
The Classification Module is executed by the Classification menu item under the
classify area to the sector menu in the tool. This command opens Classification
Form which was listed in Appendix B. Like the MCDA module, section the first
issue is that the classification module does is to check to see if the top most layer is
raster layer or not. If this criterion is not met, then buttons would not be in active
mode (Figure 4-14).
53
Figure 4-14 Reclassify form
The classification module gives the user the ability to make decision on how many
classes of the data and their intervals and their starting and ending point are.
4.8.3. Pattern Comparison Module
This module has two sub menus; first one is grid button which helps to divide the
area to the suitable number of grids and second one is pattern comparison tool that
compares each of search patterns according to their effectiveness for the case. The
effectiveness of a search pattern is determined by the search time of sweeping the
area.
This command is under comparison module. After selecting the area by rectangle
tool dividing grids, the program prompts the sweep width in kilometers and search
factor (Figure 4-15). Then the program calculates number of rows and columns
according to width and height of the area. Finally tool divides the area according to
user defined grids. User can also change grid dimensions manually.
54
Figure 4-15 Grid making form
The Search Pattern comparison module is executed by the pattern comparison
menu item under the Search Pattern menu on the tool. This command opens
FrmPatternComparision which is listed in Appendix B. Search pattern module
compare five different search patterns. The Search Pattern module (Figure 4-16)
gives the user the ability to make decision on which pattern is best fit. It controls
the effectiveness of the search.
Search speed, sweep width, search factor and height of plane affect the
effectiveness of a search operation. These parameters are used as an input for
pattern comparison form. Pattern comparison form (frmPatternComparison)
automatically gets the sweep width from the frmMakegrid. Then
frmPatternComparison form calculates total time for search and calculates
effectiveness of the selected search pattern. Lastly with the help of btnCompare
list the search effort for each search pattern. As it is known that found coordinates
of the plane, this module can calculate the time to find the target for parallel track,
creeping line and expanding square for this case.
55
Figure 4-16 Pattern comparison form
In order to provide SAR experts a practical ASAR planning tool MCDA based
GIS tool the “METUSAR” is developed, which integrated the GIS analysis and
multi criteria evaluation model. The prototype of the program is tested on a real
case study, producing conclusive performance assessment results for a particular
F-16 plane crash case.
56
CHAPTER 5
5 IMPLIMANTATION
5.1. Case Study Area
An F-16 plane crash occurred twenty kilometers south of Kütahya city in Turkey
in 16 January of 2004. Although lots of SAR team and civilian attended the search
and rescue operation, the wreckage of the plane was found nearly after three days
from the crash time which is one of the longest search periods in Turkey.
Meteorological conditions were not suitable at that time. However, to test SAR
tool, it is accepted that meteorological conditions were suitable for this case study.
According to NSAR (1998), if winds are greater than 15 knots and /or visibility is
less than 3 nautical miles (nm) but greater than 1 nm, a track spacing of 1 nm
should be considered by day or night but reduced depending on the size of the
search target.
The case study area is located at the northwest of Kütahya city in Central Anatolia
(Figure 5-1). The area is covered by four 1:25.000 scale topographical map
quadrangles of I23-d1, I23-d2, I23-d3 and I23-d4. The extents of the study area
can be defined as 4405000N, 715000E in the northwest edge and 4337000N,
736000E in the southeast edge in the zone of 35 North of Universal Transverse 3
Mercator projections. The study area is nearly 603 km 2 with dimensions of 21.7 X
27.8 km.
Some parts of the region are mountainous with a number of high hills. Most of the
area has dense forest and was covered by snow at the time of crash. The size of the
possible search area, steep slopes and changing topography did not give chance for
57
searching the area from the ground. Therefore, starting the search from the air was
a solution for this case.
Figure 5-1 Geographical location of the study area
After digitizing the crash area and its near environment, DEM of the area (Figure
5-2) was created. DEM of the area is essential for the visibility analysis steps of
the study.
58
Figure 5-2 Digital elevation Model Layer and Map layer of the area
5.2. Data Collection and Preparing Data Layers
The most important information about the crash is LKP of the target. Also, clues
from the reliable sources are important for the creation of a probability map.
(Figure 5-3) Data layers used for this case study are described in detail in
(Table 5-1). Graphical and non graphical data in Table 5-1 and 5-2 are obtained
from the Turkish Air Forces (TAF). The dataset includes plane crash information
in 2004 in Kütahya.
59
Figure 5-3 Framework of developed methodology
60
Table 5-1 shows the structure of graphical data layers. All data layers are 1/25.000
scale that enables accurate results to the SAR experts. Coordinates of each data
layer are provided from TAF and put into a map.
Table 5-1 Structure of graphical data layers
Layer
Name Data Scale Type Format Source
DEM Digital
elevation
model
1/2500 Grid Digitized
from the
map
Turkish
Air Forces
LKP Last
Coordinate
of the
plane
1/25000 Point Shape file
(*.shp)
Map
Signal 1 Signal
form the
area
1/25000 Point Shape file
(*.shp)
Map
Signal 2 Signal
From the
area
1/25000 Point Shape file
(*.shp)
Map
Settlements Villages 1/25000 Point Shape file
(*.shp)
Map
Roads Primary
and
secondary
roads
1/25000 Poly line Shape file
(*.shp)
Map
61
Table 5-2 Structure of non-graphical data layers
Data Format Source Content
LKP Coordinate
data
Turkish Air
Forces
Last known coordinates of the
plane
Signal 1 Coordinate
data
Turkish Air
Forces
Signal got from the CN-235
plane from 243.00 MHZ
Signal 2 Coordinate
data
Turkish Air
Forces
Signal got from the CN-235
plane from 282.00 MHZ
Wreckage of
Plane
Coordinate
data
Turkish Air
Forces
Coordinate of the wreckage of
the plane after the crash
Height of
Plane Number
Turkish Air
Forces
Probable height of the plane
before the crash
The LKP coordinate information of the plane was reported as 39.39.652 N and
29.35.376 E, height of the plane before the crash was reported as nearly 3000
meter (Figure 5-4). According to NSAR (1998), the actual position of the target
often has circular normal probability density distribution centered from the
reported last known position. In the light of these information and free fall
formula, the maximum reach of the plane wreckage was calculated as 6000 meters.
(Equation 5.1)
tvx
gtV
ygtegk
mVy
k
my
gk
mge
k
mVyVy
y
kt
kt
0
20
0
2
1
)1)(0(
)0(
=
=
+
+−+−=
−+=
−
−
(5.1)
62
Where ;
Vy: Initial velocity of the plane on the y axis
Vy0: Initial altitude of the plane
g: Gravity acceleration
m: Mass of the object
t: Fall time
x: Range
Figure 5-4 Range of the air plane
Statistically, the amount of probability of contained (POC) in a circle drawn from
the last known position to reach the plane wreckages distribution is given in
Equation 5.2 (Cooper et al., 2003).
63
2/2
1 RePOC −= (5.2)
Where, e is the base for natural logarithm. The probability of containing one
standard deviation of the mean is about 68 %, two standard deviation of the mean
is 27 %, three standard deviation of the mean is 4 % and the remaining part of the
area is accepted as 1 % (Figure 5-5).
Figure 5-5 Areas with certain probabilities around last known position
64
5.2.1. Signal Points
After the crash and before finding the wreckage of the plane, some signals and
words of witnesses were reported to the search planning center. One of the dense
signals was reported from the CN-23 aircraft from 243 MHZ (39.43 N and 29.38
E) (Gerede, 2007).
The other point of signal was again reported from the CN-23 Aircraft from the
channel 8 and 282.8 MHZ. Considering all the suggested safe distances in the
NSAR (1998) and expert suggestion, minimum distances for the signal points are
determined as 5 km from the centers. These distances are used to create buffer
zones around signal points and included to the study area (Figure 5-6).
Figure 5-6 Location of signal points
65
5.2.2. Settlement Layer
After digitizing all the settlements in the area, visibility analysis was made from
the center of the each settlement. Considering all the suggested safe distances in
the NSAR (1998), minimum distances for the study area are determined as 5 km
for settlements. These distances are used to create buffer zones around settlement
areas and excluded from the study area.
The next step is intersecting the visibility of the sight of the settlements and
buffers from their centers. It is assumed that, if the wreckage of the plane is in the
buffer zones within 5 km radius from the center of settlements, it is already seen
by the people living in the settlements over there. Therefore; black areas should
not be searched by the search team shown in Figure 5-8. Also, height of the
visibility is tested aside from formal human height. It gives much large areas
effecting probability map. Therefore, formal human height is used for visibility
map creation.
66
Figure 5-7 Visibility of Settlement layer of the study area
Figure 5-8 Intersection of visibility map and settlement buffers
5.2.3. Roads Layer
The same process is repeated for the road layer which is digitized from the
1:25.000 scaled topographical maps. As in the case of settlement areas, it is
intersected with the visibility layer. The buffer zone from the roads is accepted as
100 meters, since it is accepted that if the wreckage of lost F-16 is in the buffer
zone of the roads, it could be seen by the people passing over there. (Figure 5-9)
67
Figure 5-9 Visibility map from the view of roads
Figure 5-10 Intersection of visibility map and road buffers
5.3. Construction of Probability Maps based on MCDA
The most important part of this search operation is having a consistent probability
map. Ranking the layers according to their importance and rating them was done
by search expert from the Turkish Air Forces, Major Ali Gerede. Three different
MCDA methods were tested while producing probability map.
5.3.1. Simple Additive Weight (SAW)
Weights given by the expert is showed in Table 5-3. The highest score is given to
last known position of the plane.
68
Table 5-3 SAW ranking
Layers Weights
Last Known Position 0.6
Signal 1 0.1
Signal 2 0.1
Visible range of roads 0.1
Visible range of villages 0.1
The tool normalizes the values given by experts and according to the values; the
first probability map of the area is created (Figure 5-11). As expected center of the
produced probability map according to SAW decision rule (Figure 5-11) contains
higher probability area hence search expert gives 60 % of the weight to LKP. Also
signal points are easily distinguished from the environment of them. The lowest
probabilities are shown in the centers of settlement areas. Probabilities in the area
are between 0.0019 and 0.0996. According to result of this decision rule the center
of the area in white color and three outer circles contains 32 % of the total
probability.
69
Figure 5-11 Probability map produced with SAW method
5.3.2. Analytic Hierarchy Process (AHP)
Difference of the AHP method is based on pair wise comparison. With the help of
this tool every layer of the study can be seen in a matrix and the expert compared
them for producing probability map (Table 5-4). Probability map according to the
result of AHP decision rule is shown in Figure 5-12. Probabilities in the area are
between 0.0017 and 0.106. According to the result of this decision rule, the center
of the area in white color and three outer circles contain 36 % of the total
probability.
70
Table 5-4 AHP method criteria
Figure 5-12 Probability map produced from the AHP method
Preference Value Equally 1 Equally to moderately 2 Moderately 3 Moderately to strongly 4 Strongly to very strongly 5 Very strongly 6 Very extremely strongly 7 Extremely 8
71
According to values in Table 5-4 the tool produced the probability map in Figure
5-12
5.3.3. Ordered Weighted Averaging (OWA)
OWA decision rule determines weights according to list of layers and number of
layers. Expert can make an order from the most preferable to a least preferable
layer. The values of OWA are shown in Table 5-5:
Table 5-5 Preferences of Search Expert according to OWA method
Layers Weights Strategy Order
LKP 0.61 1 Most Preferable
Signal 1 0.146 2 …
Signal 2 0.098 3
Visible range of roads 0.077 4
Visible range of villages
0.064
Moderately Optimistic
5 Least Preferable
According to values in Table 5-5 the tool produced the probability map as shown
in Figure 5-13. Probabilities of founding wreckage of the plane in the area for one
cell are between 0.0019 and 0.108. According to the result of this decision rule, the
center of the area in white color and three outer circles contains 36 % of the total
probability. The rest of the area contains 64 % of total the probability. On the other
hand, road and settlements buffers in black contain lesser probabilities.
72
Figure 5-13 Probability map produced from the OWA method
Although the results of three methods gave visually the same pattern, there are
some distinctions among them. The resultant probability maps according to three
methods of MCDA show that the SAW method gives the maximum probability
(between 0.1216 and 0.002) the second method OWA (between 0.11136 and
0.00199) and the third AHP method between (0.106397 and 0.001738). Total
probability in the center of the circles of SAW method is higher, as search expert
gave the maximum weight to LKP (60 %). Similarly, OWA method calculates the
weight according to order and number of layers it gives the (61 %) weight to the
LKP. Lastly, AHP method gives less weight according to these methods. After
73
applying three different MCDA methods on the case, expert started classification
each production with different classification intervals.
5.4. Reclassification of the Probability map
Reclassification of the area gives ability to divide the area according to classified
segments. Expert examined different classification intervals for every probability
maps produced from the MCDA methods. Firstly, the probability map was
produced from the SAW method and was classified with different values.
The aim of this procedure is testing different classification intervals for probability
maps. Resulted probability maps were classified into three, four, five, six, seven,
eight and nine intervals. Used classification method is natural breaks, and then the
last classes are widened manually to encompass the entire lower half of the ranked
values. Also, the upper classes are restricted to the highest ranking value.
Effects of classification determining the sub search areas are a critical question.
Taking into consideration each decision rule results, each decision rule is classified
into 3, 4, 5, 6, 7, 8 and 9 plots intervals. Every decision rule gives different pattern
and these patterns provide detailed and reliable information for the expert to divide
the search area into search sectors.
74
5.4.1. Reclassification According to Result of SAW
Figure 5-14 shows the case when the area is classified into 3 classes which form a
preference scale with three degrees of decreasing probability. The center of the
circle colored by red has the maximum probability. Yellow areas have contained
minimum probability. The weakness of this figure is the second maximum
probability area covers almost the whole area (72 %). Therefore using Figure 5-14
would not give an optimal solution.
Figure 5-14 SAW classification with 3 classes
Figure 5-15 shows the case when the area is classified into 4 classes. It covers
much more area in the center of the circle than Figure 5-13. Thus the effectiveness
of this classification is better. Also, road weights decrease the probability of the
center area in red color. However, signal points can not be discriminated from this
classification. Since used classification method is natural breaks, the second plot
in the histogram of Figure 5-15 resulted as 0.
75
Figure 5-15 SAW classification with 4 classes
In Figure 5-15 Probability of Area (POA) is higher than 3 classes. Also a signal
point is shown in this figure. Figure 5-16 gives a similar result with Figure 5-15.
However, intersection of road and settlement buffers can be discriminated easily.
76
Figure 5-16 SAW classification with 5 classes
Figure 5-17 SAW classification with 6 classes
Hence, Figure 5-17, 5-18 and 5-19 have nearly equal areas and their effectiveness
are nearly the same. However, signal points are not discriminated in Figure 5-18.
Figure 5-18 SAW classification with 7 classes
77
Figure 5-19 and Figure 5-20 differentiate the other reclassification figures hence
signal points can be easily discriminated from the others.
Figure 5-19 SAW classification with 8 classes
Figure 5-20 SAW classification with 9 classes
78
5.4.2. Reclassification According to Result of AHP
Result with classification into 3 classes for AHP method is shown in Figure 5-21
and the center of the circle colored by red has the maximum probability. Second
maximum probability area covers a reasonable area. Therefore using this figure
would be optimal. Figure 5-22 is the case with classification into 4 classes. It
covers longer area in the center of the circle compared to Figure 5-21. Thus the
total effort for this classification is higher than Figure 5-21.
Figure 5-21 AHP classification with 3 classes
79
Figure 5-22 AHP classification with 4 classes
Results of classification into 5, 6, 7 and 9 classes for AHP method are shown in
Figure 5-22, Figure 5-23, Figure 5-24, Figure 5-25 and Figure 5-26 respectively.
Although there are slight differences, probability of areas are just about the same.
Thus the total effort for these classifications is less than Figure 5-20 and Figure
5-22.
Figure 5-23 AHP classification with 5 classes
80
Figure 5-24 AHP classification with 6 classes
Figure 5-25 AHP classification with 7 classes
81
Figure 5-26 AHP classification with 9 classes
5.4.3. Reclassification According to Result of OWA
Results of OWA method is slightly the same apart from Figures 5-28 and 5-33.
Figures 5-27, 5-29, 5-30, 5-31 and 5-32 give nearly the same search efforts.
Figure 5-27 OWA classification with 3 classes
82
Figure 5-28 OWA classification with 4 classes
Figure 5-29 OWA classification with 5 classes
83
Figure 5-30 OWA classification with 6 classes
Figure 5-31 OWA classification with 7 classes
84
Figure 5-32 OWA classification with 8 classes
Figure 5-33 OWA classification with 9 classes
85
Important criteria for comparing the results are as follows:
• Signal points should be discriminated easily by the search expert,
• Road and settlement buffers should be discriminated from the reclassified
map
• Size of the searching area should be between (5 x 5 km) and (10 x 10) km
Considering the above criteria Figure 5-17 and Figure 5-20 are suitable for SAW
decision rule. Figure 5-26 is suitable For AHP decision rule and Figure 5-28,
Figure 5-31 and Figure 5-33 are suitable for OWA method.
5.4.4. Dividing area into grids
Sweep width (w) value for this case is considered as 0.7 km according to values in
Table A-3 listed in Appendix A.
User can determine the border of the search area with the help of rectangle tool in
‘ARCGIS’ toolbar. After drawing the area as a rectangle from the menu of search
tool, the user can determine the number of rows and columns according the size of
one side of the rectangle. User can also use different rectangles. However every
time it should be calculated and summed up to calculate search time of search
patterns.
Also the search area can be divided into more than one segment. In this case,
search time for every segment should be calculated separately and added to
calculate total time for the search with a chosen search pattern.
86
5.5. Discussions
The implications of this study are composed of three tests; which are listed below:
• Test of finding suitable MCDA methods: Three different MCDA methods
(SAW, AHP and OWA) were tested according to search expert priorities.
For each decision rule the changes of probability distribution were also
examined.
• Test of class numbers while dividing area into sub search areas: every
decision rule affecting the classification of the probability map was
examined.
• Test of finding suitable search patterns for this kind of cases and
environmental effects: for every probability distribution map five different
search patterns were tested based upon their effectiveness.
Result of these tests has its preferred use conditions and one must understand the
methods in order to choose the best one for the same conditions. All are useful for
the same purpose of defining an importance rating over a range of criteria. SAW
can only be used when the decision maker knows the exact percentage of each
criterion contributing to the decision (Malczewski, 1999). AHP is most valuable
when there is a great uncertainty and there are opposing forces at work in the
decision process (Saaty, 1990). The OWA method should be used when the
concept of decision risk makes sense in the decision process (Yager, 1988).
According to results of test of finding suitable MCDA methods, probability map
determined by AHP decision rule provides more effective results than other
decision rules. The smallest search effort values are calculated in AHP decision
87
rule for every search pattern. From the Figure 5-34, it can be said that the most
effective results were provided by the AHP method.
Average Test Results
0
100
200
300
400
500
600
700
800
900
1000
Average SAW Average AHP Average OWA
Average deciison rules values
Searc
h e
ffo
rt
Paralel Creeping Expanding Sectorsearch
Figure 5-34 MCDA and Search Pattern Comparison
Figure 5-34 also shows effectiveness of search patterns with MCDA decision
rules. The most effective search patterns are both Expanding Search Pattern and
Sector Search Pattern. The less effective search patterns for this case are Parallel
search pattern and Creeping Line Search Pattern. The considerable difference
among these search patterns originated from their commence point to the search.
Expanding Search Pattern and Sector Search Pattern start from the SAR operation
from the center point of the POA. According to results seen in Figure 5-34
Creeping Search pattern method is the least effective search pattern. Hence, in that
pattern the plane sweeps the area two times for every line. This increases the POD
however it also increases the search time. Therefore search effectiveness of this
pattern is less than other search patterns.
88
As it is discussed in Chapter 1, Search Effort (Z) is the product of the sweep width
times the length of track in nautical miles in the search area. While testing search
patterns efficiency, Z is used for this case. Coverage factor is not used, because the
extent of the search areas for each pattern is equal.
Search effort results for SAW method is shown in Figure 5-35. While calculating
search efforts, program uses track line length and sweep width of the pattern as
input. Generally, in SAW decision rule average search effort values were high
(between 281 and 859). The highest search efforts for SAW method is 7 plot
intervals in creeping line search pattern, 5, 8 and 9 plot intervals are also above the
average search effort.
SAW decision rule Search Effort Results
0
200
400
600
800
1000
1200
1400
1600
Se
arc
h E
ffo
rt Paralel
Creeping
Expanding
Sectorsearch
Paralel 10,959 162,71 849,5 303,88 898,78 849,5 849,5 560,69
Creeping 15,236 234,69 1269,4 492,27 1369,6 1318,6 1318,6 859,771
Expanding 5,5565 81,651 425,42 154,77 450,08 426,1 426,1 281,383
Sectorsearch 5,6968 91,149 488,78 173,09 517,31 488,78 488,78 321,941
SAW-3 SAW-4 SAW-5 SAW-6 SAW-7 SAW-8 SAW-9 Average
Figure 5-35 Search Efforts (Z) for SAW
Figure 5-36 shows search effort values for AHP decision rule. Search effort values
are nearly the same except 4 plot intervals. The average search effort values are
(between 153,745 and 466). These values are nearly half of SAW method results.
89
AHP decision rule Search Effort Result
0
200
400
600
800
1000
1200
1400
1600
Searc
h E
ffo
rt Paralel
Creeping
Expanding
Sectorsearch
Paralel 280,92 898,78 162,71 162,71 303,88 162,71 162,71 304,917
Creeping 467,84 1369,6 234,69 234,69 492,27 234,69 234,69 466,924
Expanding 144,76 450,08 81,651 81,651 154,77 81,651 81,651 153,745
Sectorsearch 160,62 517,31 91,149 91,149 173,09 91,149 91,149 173,659
AHP-3 AHP-4 AHP-5 AHP-6 AHP-7 AHP-8 AHP-9 Average
Figure 5-36 Search Efforts (Z) for AHP
According to the results shown in Figure 5-37 average search effort values are
between AHP and SAW decision rules. Search effort values are nearly the same
except from 4 and 9 plot intervals. The average search effort values are (between
215 and 660).
OWA decision rule Search Effort Results
0
200
400
600
800
1000
1200
1400
1600
Searc
h E
ffo
rt Paralel
Creeping
Expanding
Sectorsearch
Paralel 303,8 897,39 176,57 176,57 350,76 192,29 898,78 428,023
Creeping 492,27 1420,7 248,38 248,38 567,91 278,53 1369,6 660,824
Expanding 154,77 451,47 88,905 88,905 178,42 96,468 450,08 215,574
Sectorsearch 173,09 517,31 99,337 99,337 200,2 108,6 517,31 245,026
OWA-3 OWA-4 OWA-5 OWA-6 OWA-7 OWA-8 OWA-9 Average
Figure 5-37 Search Effort (Z) for OWA
90
According to the results shown in Figure 5-38, the tool was tested for every search
pattern. While calculating search time for each search pattern, weather conditions
were assumed suitable from the air search. Figure 5-38 shows both test results of
the decision rules according to search effort and test of finding suitable search
patterns. Expanding and Sector search patterns are the most effective search
patterns for five, six and eight plot intervals.
0
200
400
600
800
1000
1200
1400
1600
SAW
-3
SAW
-4
SAW
-5
SAW
-6
SAW
-7
SAW
-8
SAW
-9
Ave
rage
AHP-3
AHP-4
AHP-5
AHP-6
AHP-7
AHP-8
AHP-9
Ave
rage
OW
A-3
OW
A-4
OW
A-5
OW
A-6
OW
A-7
OW
A-8
OW
A-9
Ave
rage
Paralel Creeping Expanding Sectorsearch
Figure 5-38 The result of comparing three decision rules
In the real life, operation for this case was driven out three days (Gerede, 2007).
Except the use of GIS, TAF used expanding square search pattern in the search
operation. The basic obstacle was weather conditions. In this study it is accepted
that weather conditions are suitable for air search and rescue operation.
Results of our study showed that both expanding square and sector search pattern
provide an effective result for this case. This study is differentiated from TAF’s for
the used methodologies. Basically, GIS is used as a main tool for data analysis,
MCDA for weighting input layers. The last steps of both are nearly the same.
91
Moreover, results of this study supports used search patterns in the real SAR
operation and recommend sector search pattern for such operations.
92
CHAPTER 6
6 CONCLUSION AND RECOMMENDATIONS
6.1. Conclusion
The adopted methodology, which integrates GIS, MCDA and Search Theory, for
SAR planning leads to some considerable results. The conclusions derived from
the study are as follows:
• When MCDA decision rules are tested, it is found that AHP provides much
better results than SAW and OWA. This confirms that the pair wise
comparison is more reliable for these conditions.
• Of the used three different MCDA decision rules, SAW decision rule
provides the worst search effort results. The average search effort of SAW
is considerably higher than both OWA and AHP. Therefore, OWA and
AHP decision rules should be preferred by the search expert in ASAR
cases.
• While dividing area into sub sectors; reclassification method which is
firstly adopted for SAR operations is used. This method provides search
expert reliable borders of the sub sectors in the probability mapping. This
method also decreases the subjectivity of search expert while dividing area
into sub search areas.
• According to the results obtained from the reclassification of probability
distribution maps, the reasonable class numbers are determined as 5, 6, 7, 8
93
and 9 plot intervals for AHP decision rule. 3, 4 plot intervals are not
reasonable for this situation.
• OWA decision rule has a similar character to AHP decision rule. The
reasonable class numbers are determined as 5, 6, 7 and 8 plot intervals like
AHP. However, besides 3, 4 plot intervals 9 plot interval is not reasonable
for OWA decision rule.
• On the other hand, for SAW decision rule 4 and 6 plot intervals are
reasonable. Hence 3,5,7,8 and 9 plot intervals provide higher search effort,
they can not be considered as a reasonable solution.
• In the final stage, a functional comparison analysis of search patterns
within the framework of search theory in terms of time domain, it is found
that expanding square and sector search patterns provide shortest time to
sweep the whole area.
• The main difference among these search patterns is commencing search
points. Expanding square and sector search patterns started searching the
area from the LKP point. Therefore, if LKP of the plane is known, it is a
better solution to use Expanding square and sector search patterns.
• In this case, target is found around LKP, therefore giving the highest score
to the LKP while weighting was reasonable.
• As a result with this methodology, the whole search area can be swept by
the search aircraft in the limit of weather condition. Finding the location of
missing aircraft in a short time is an achievement of developed
methodology.
94
6.2. Recommendations
There are many modules and tasks that METUSAR tool could be expanded.
Following recommendations can be useful for similar and further studies.
• In this study generic search patterns explained in NSAR (1998) were used.
Therefore search expert was limited to find the best fitting search pattern to
the area. For further studies, a search pattern which is suitable for the area
automatically.
• Also, topographic obstacles were not encountered in determining the
probability maps. It would be beneficial to assess topographic features of
the area apart from generation of visibility maps.
• Aspects variations (Appendix C) should be taken into account while
calculating search effectiveness. Because, the speed of the plane could be
adjusted and this can affects the search effectiveness. In smaller areas
aspects and special characteristics of the terrain should be considered. Also
lots of parameters in smaller areas could be included in the study like size
of object, composition of the surface, composition of the object (color,
reflective ability) and vegetation (so many tree types, and not growing in
uniform pattern).
• This tool only covers a small part of a search and rescue operation
processes. Also, optimal ways of reaching the missing object is a new
study subject.
• The features listed in this part are just suggestions of directions the
METUSAR tool can be taken in the future works. Any of these extensions
would increase the functionality and usability of this tool in real life cases.
95
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Burke, R., 2003, “Getting to Know ArcObjects”, ESRI Press, California, 422
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Byeong, S., A., 2006, “On the Properties of OWA Operator Weights functions
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Cadre, J.P., Soiris, G., 2000, “Searching Tracks” IEEE Transactions on Aerospace
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Champagne, L., Carl, R.G., Hill, R., 1999, “Search Theory, Agent-Based
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Cooper, D.C., Frost, J.R., Robe, R. Q., (2003), “Compatibility of Land SAR
Procedures with Search Theory” Report prepared for United States Coast
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Gerede, A. 2007, Personel communication.
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IAMSAR, 2001 “International Aeronautical Search and Rescue Manual, 2001”
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Kenneth, M., 1973, “Multi Criteria Decision Making”, Kingsport Press, Columbia,
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Malczewski, J., 1999, “GIS and Multi-criteria Decision Analysis”, John Wiley and
Sons, Newyork, pp. 15-285.
NSAR Manual, 1998, “National Search and Rescue Manual, B-GA-209-001/FP-
001 DFO 5449”
O,Connor, D., 2004, “Controversial Topics in Inland SAR planning”, Northeast
Wilderness Search and Rescue, Report, 16 pages.
Pomerol, J.C., 2000, “Multicriterion Decision in Management: Principles and
Practice”, Kluwer Academic Press, 396 pages.
Richardson, H., R., 1971, “Optimal Search with Uncertain Sweep Width”,
Operations Research, Vol. 20, No. 4. (Jul. - Aug., 1972), pp. 764-784
Richardson, H.,R., Corwin, T., 1980, “Computer-assisted search”
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for land searches”, Potomac management group, Virginia, 125 pages.
Roper-Lowe, G., C., Sharp, J., A., 1990, “The Analytic Hierarchy Process and its
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Saaty, T.L., 1990. Multi Criteria Decision Making: The Analytic Hierarchy
Process. RWS Publications, Pittsburgh.
Stone, D.L., 1975, “Theory of Optimal Search”, A Series of Monographs and
Textbooks, Academic Press, 260 pages.
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Stone, D.L., Wagner, D.H., 1979, “Search Theory: A Mathematical Theory for
Finding Lost Objects ”, Mathematics Magazine, Vol. 50, No. 5, 248-256.
Stone, D.L., 1983, “The Process of Search Planning: Current Approaches and
Continuing Problems”, Operations Research, Vol. 31, No. 2, 207-233.
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Found”, The Journal of the Operational Research Society, Vol.48, No.1,
44-50.
USADT, 2001, “Department of Transportation final report”, “Advances and
Applications to Search and Rescue Decision Support”, Sprinfield, 60
pages.
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Web 1, Wikipedia, Last Accessed 31 March 2007, from the World Wide Web:
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from the World Wide Web, http://www.sarinfo.bc.ca/SARDefinitions.htm
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overview” Last accessed 06 April 2007 from the World Wide Web,
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003.htm
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Web 4, ESRI site, Last Accessed 10 October 2006, from the World Wide Web:
http://www.esri.com/software/arcgis/about/desktop.html
Wollan, H., 2004, “Incorporating Heuristically Generated Search Patterns in
Search and Rescue”, Ms. Thesis, University of Edinburgh, 52 pages.
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100
APPENDIX A
A VARIABLES FOR SPECIFIC SEARCH TYPES
Table A-1 gives track speed and altitudes for electronic and visual searches. In this
study, value of Search is chosen for SAR input variables. In the real case, search
was conducted both day and night in order to find wreckage of the plane.
Table A-1 Search Types and Altitudes (IAMSAR, 2001)
Table A-5 shows sweep widths and search factor for a specific condition. Table
A-3 shows search correction factors according to properties of the terrain. In this
case 60-85 % of the area was mountainous and covered by snow.
101
Table A-2 Sweep width for visual searches (km/nm) (IAMSAR, 2001)
Table A-3 Search correction factors (IAMSAR, 2001)
Table A-4 Recommended search altitudes (IAMSAR, 2001)
102
Table A-5 Sweep width table
It is important for pilots to reports what they can observe, including:
• Meteorological visibility (6 km in haze)
• Amount of vegetation (50 % moderate three cover in valleys with low
brush elsewhere)
103
• Nature of the terrain (hilly with numerous rocky outcrops visible in un
forested areas)
• Weather (overcast, light rain, wind)
• Any other observations that might effect estimates of search effectiveness,
the subject’s continued chances of survival or decisions about whether to
deploy ground parties. Sightings are particularly important.
104
APPENDIX B
B.1 OVERVIEW
This Appendix lists the Forms, Class modules and functions that were used in the
program (Table B-1). To assist the reader in understanding this code, textual
notation is used in the body of the code.
Table B-1 Summary Information of used GUI
Form Name Class Module Function Page FrmAHP ClsAHP Contains AHP
decision rule GUI 51
FrmSAW ClsSAW Contains SAW decision rule GUI
49
FrmOWA ClsOWA Contains OWA decision rule GUI
51
FrmPatternSelection ClsPatternSelection Provide a form to a user making selection appropriate search pattern.
54
FrmSector ClsSector Provide probability map into smaller sectors
53
FrmTrack ClsTrack Provides reclassifying probability maps
52
B.2 MapAlgebraClass for AHP
This script performs main calculations about Multi Criteria Decision Analysis
(MCDA).
105
Code for frmAHP form
Variables
Private m_MxDoc As IMxDocument
Private m_Map As IMap
Private m_Outraster As String
Private m_Extension As String
Private m_App As IApplication
Private m_InRaster As IRaster
Private m_CatTable As IRasterCatalogTable
Private m_Outws As IWorkspace
‘The code above is necessary for being communication with arc map
Functions
Private Sub WieghtedSum()
All of the calculations are done in this piece of code. User inputs get by the help of
text boxes in the forms and used as a double variable.
Dim n As Double ‘ n is the number of compared layer Dim lamda As Double ‘ lamda is an average of layer values
Dim CI As Double ‘ CI is used for Consistency index
Dim RI As Double ‘RI is used for random index
n = 5 '
lamda = (cra + crb + crc + crd + cre) / 5
CI = (lamda - n) / (n - 1)
RI = 1.12 ' this is constant for n=5 layers
cr = CI / RI
Text17.Text = cr
If cr < 0.5 Or cr = 0.5 Then
MsgBox "Decision is relatively Consistent"‘Prompts the user about Consistency of
choices
106
ElseIf cr < 1 And cr > 0.5 Then
MsgBox "Your Decision is almost Consistent" Prompt the user about Consistency of
choices
Else
MsgBox "Your Decision is not seem consistent it is very high try again"
End If
End Sub
Get list values to the Form
List3.AddItem "Last Known Position"
List3.AddItem "Cities"
List3.AddItem "Roads"
List3.AddItem "Second Position"
List3.AddItem "Direction"
Command2.Enabled = True
Private Sub Command2_Click()
Call WieghtedSum
Command4.Enabled = True
End Sub
Private Sub Command3_Click()
Algorithm for ARCMAP communication
Dim m_MxDoc As IMxDocument
Dim m_Map As IMap
Dim pLayer As ILayer ‘ Creates Layers
Set pLayer = m_Map.Layer(i) ‘ Sets the value of Layer
Dim pRLayer As IRasterLayer
'Create a Spatial operator
Dim pAlgbOp As IMapAlgebraOp
Set pAlgbOp = New RasterMapAlgebraOp
‘Set output workspace
Dim pEnv As IRasterAnalysisEnvironment
107
Set pEnv = pAlgbOp
Dim pWS As IWorkspace '
Dim pWSF As IWorkspaceFactory
Set pWSF = New RasterWorkspaceFactory
Set pEnv.OutWorkspace = pWS
'Bind a raster
Dim Grid1 As String
Call pAlgbOp.BindRaster(pInRaster, "Grid1")
Call pAlgbOp.BindRaster(pInRaster2, "Grid2")
Call pAlgbOp.BindRaster(pInRaster3, "Grid3")
Call pAlgbOp.BindRaster(pInRaster4, "Grid4")
Call pAlgbOp.BindRaster(pInRaster5, "Grid5")
'Perform Spatial operation
Dim pvalue As String
pvalue = Text6(0).Text
Dim strExpression As String
strExpression = "([Grid1] * " & pvalue & ") + ([Grid2] * " & pvalue2 & ")+ ([Grid3] * " & pvalue3
& ")+ ([Grid4] * " & pvalue4 & ")+ ([Grid5] * " & pvalue5 & ")"
Dim pOutRaster As IRaster
Set pOutRaster = pAlgbOp.Execute(strExpression)
Dim pRlayer6 As IRasterLayer
Set pRlayer6 = New RasterLayer
pRlayer6.CreateFromRaster pOutRaster
‘Add created layer in to ARCMAP
m_Map.AddLayer pRlayer6
108
B.3 MapAlgebraClass for SAW
Code for frmAHP form
The difference of these forms is getting of variables and calculations. Basically,
SAW Form gets the SAR expert’s preferences, and then processes it according to
SAR decision rule.
Variables
Dim m_MxDoc As IMxDocument Dim m_Map As IMap Dim pLayer As ILayer Dim pLayer2 As ILayer Dim pLayer3 As ILayer Dim pLayer4 As ILayer Dim pLayer5 As ILayer
Functions
'Creating a Spatial operator
Dim pAlgbOp As IMapAlgebraOp Set pAlgbOp = New RasterMapAlgebraOp
' Set output workspace Dim pEnv As IRasterAnalysisEnvironment Set pEnv = pAlgbOp Dim pWS As Iworkspace
Dim pWSF As IWorkspaceFactory
Set pWSF = New RasterWorkspaceFactory Set pEnv.OutWorkspace = pWS
' Bind a raster Dim Grid1 As String Call pAlgbOp.BindRaster(pInRaster, "Grid1") Call pAlgbOp.BindRaster(pInRaster2, "Grid2") Call pAlgbOp.BindRaster(pInRaster3, "Grid3")
109
Call pAlgbOp.BindRaster(pInRaster4, "Grid4") Call pAlgbOp.BindRaster(pInRaster5, "Grid5")
'Perform Spatial operation
Dim pvalue As String Dim pvalue2 As String Dim pvalue3 As String Dim pvalue4 As String Dim pvalue5 As String pvalue = Text6.Text pvalue2 = Text7.Text pvalue3 = Text8.Text pvalue4 = Text9.Text pvalue5 = Text10.Text
Dim strExpression As String strExpression = "([Grid1] * " & pvalue & ") + ([Grid2] * " & pvalue2 & ")+ ([Grid3] * " & pvalue3 & ")+ ([Grid4] * " & pvalue4 & ")+ ([Grid5] * " & pvalue5 & ")"
Dim pOutRaster As IRaster Set pOutRaster = pAlgbOp.Execute(strExpression) Dim pRlayer6 As IRasterLayer Set pRlayer6 = New RasterLayer pRlayer6.CreateFromRaster pOutRaster Private Sub Command2_Click() Text6.Text = a Text7.Text = b Text8.Text = c Text9.Text = d Text10.Text = e cmdOk.Enabled = True
End Sub
Dim i As Integer
For i = m_Map.LayerCount - 1 To 0 Step -1 Set pLayer(i) = m_Map.Layer(i) Next i Dim pRLayer() As IRasterLayer Dim pInRaster() As IRaster ReDim pRLayer(Layersayisi) ReDim pInRLayer(Layersayisi) Dim g As Integer
110
For g = (m_Map.LayerCount - 1) To 0 Step -1 Set pRLayer(g) = pLayer(g) Set pInRaster(g) = pRLayer(g).Raster
Next g End Sub
B.4 POA SegmentModule
This scripts used for accessing the pixel value of the grid. AcessValueOfRaster
function accesses raster table of the grid and gets pixel values of raster cells.
Secondly determines minimum and maximum pixel values of the grid and divide it
user defined value. Finally remaps old values to a user defined values.
Sub AccessValueOfRasterTable(pRaster As IRaster, sFieldName As String, RowIndex As Integer)
End Sub
'Create a raster descriptor and specify the field to be used for reclassify
Dim sFieldName As String
sFieldName = "Value"
Dim pRasDescriptor As IRasterDescriptor
Set pRasDescriptor = New RasterDescriptor
pRasDescriptor.Create pInRaster, Nothing, sFieldName
'Create a RasterReclassOp operator
Dim pReclassOp As IReclassOp
Set pReclassOp = New RasterReclassOp
'Create a StringRemap object and specify remap
Dim pNumberRemap As INumberRemap
Set pNumberRemap = New NumberRemap
Dim mins As Double
Dim maxs As Double
'Call AccessValueOfRasterTable(pInRaster, "Value", NumOfValues – 1)
111
MinValue = -1
Dim MaxValue As Double
MaxValue = pLayer.Value
fark = (MaxValue - MinValue) / com
For i = 1 to com
pNumberRemap.MapRange MinValue, (MinValue + fark), i
MinValue = MinValue + fark
Next i
pNumberRemap.MapRangeToNoData 100, 200
End Sub
B.5 SearchPatternComparison module
This module divides the area in to user defined grids. Firstly user defines a
rectangle.
' Create the RasterExtractionOp object
Dim pExtractionOp As IExtractionOp
Set pExtractionOp = New RasterExtractionOp
' Declare the input dataset object
Dim pInputDataset As IGeoDataset
' Call function to open a dataset
Dim pLayer As ILayer
Set m_MxDoc = m_App.Document
' Create an envelope (minimum rectangle area of the selected object)
Set pEnvelope = New Envelope
pEnvelope.PutCoords dblXMin, dblYMin, dblXMax, dblYMax
Set pOutputDataset = pExtractionOp.Rectangle(pInputDataset, pEnvelope, True)
End Sub
Function that generate grid based on draved envelope
Public Function GenerateGrid(strWorkPath As String, strName As String, pEnv As IEnvelope,
End Function
Function that convert envelope to polygon
112
Private Function Env2Polygon(pEnv As IEnvelope) As IPolygon
End Function
Functions that making result grids
Private Function MakeFields(pEnv As IEnvelope) As IFields
End Sub
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APPENDIX C
C ASPECT VARIATIONS OF THE AREA
Figure C-1 Aspect variations of the area
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